WO2025191822A1 - Autonomous moving body, autonomous travel method, and autonomous travel program - Google Patents

Abstract

An autonomous moving body (10) is equipped with a periphery detection sensor (203) and autonomously travels. A self-position estimation unit (110) uses the periphery detection sensor (203) to estimate the position and orientation in a point cloud map as a self-position orientation. A self-position determination unit (120) performs a determination process for determining whether or not the self-position orientation is normal. When the self-position orientation is determined to be normal, a travel control unit (130) uses the self-position orientation to control autonomous travel. When the self-position orientation is determined not to be normal, the self-position estimation unit (110) performs an acquisition process for acquiring a normal self-position orientation, and repeats the acquisition process until the self-position orientation obtained by the acquisition process is determined by the determination process to be normal.

Description

Autonomous traveling vehicle, autonomous traveling method, and autonomous traveling program

This disclosure relates to an autonomously traveling vehicle, an autonomous traveling method, and an autonomous traveling program. In particular, it relates to an autonomously traveling vehicle that travels autonomously based on technology such as SLAM (Simultaneous Localization and Mapping), which uses surrounding detection sensors to estimate its own position and orientation on a point cloud map.

Development is underway for personal mobility vehicles such as PMVs and AMRs, which are mobile robots capable of autonomously driving within facilities. PMV is an abbreviation for Personal Mobility Vehicle. AMR is an abbreviation for Autonomous Mobile Robot. The development of small mobile vehicles such as PMVs and AMRs has the following aims: to supplement the last mile of autonomous driving for automobiles, to provide assistance to those with limited mobility who do not own cars, to be used in facilities where cars cannot enter, or to provide unmanned services within facilities. PMVs and AMRs estimate their self-location using LiDAR-SLAM.

PMVs and AMRs are expected to be used both indoors and outdoors. However, when a PMV or AMR is equipped with a gyro sensor for high-precision attitude measurement, such as an optical fiber gyroscope, or an odometer that improves the accuracy of the distance traveled by attaching a vehicle speed sensor to the wheels or drive shaft, there are concerns that the installation space required will increase the size of the vehicle, narrowing the range of use, and that the installation of such equipment will hinder price reduction.
Furthermore, if self-location estimation is performed using only LiDAR-SLAM, the self-location estimation will be incorrect if the surrounding terrain is similar. Furthermore, when traveling between indoors and outdoors, if self-location estimation is performed using only GNSS reception data, the error will be large except for FIX (position information on the centimeter level), resulting in an incorrect self-location. GNSS is an abbreviation for Global Navigation Satellite System.

Patent Document 1 discloses a technology that combines the probability distribution of a vehicle's own position determined by GNSS reception data and that determined by point cloud data from LiDAR, and determines the peak position of the combined SLAM/GNSS probability distribution as the vehicle's own position. SLAM is an abbreviation for Simultaneous Localization and Mapping.

Japanese Patent Application Laid-Open No. 2018-206004

SLAM, which uses a surrounding detection sensor such as LiDAR, can create a point cloud map (also known as an environmental map) and estimate the vehicle's position and orientation with high accuracy and low cost without relying on GNSS reception data. Therefore, there is a demand for self-position estimation based on SLAM, which uses a surrounding detection sensor.
On the other hand, the technology of Patent Document 1 combines self-location estimation using GNSS reception data with self-location estimation using LiDAR-SLAM, which poses a problem in that it is not possible to perform self-location estimation based on SLAM, which uses a surrounding detection sensor.

The autonomous vehicle disclosed herein aims to return to SLAM using a surrounding detection sensor by performing self-location estimation using another method if self-location estimation using SLAM using a surrounding detection sensor fails.

The autonomous traveling vehicle according to the present disclosure is an autonomous traveling vehicle equipped with a surrounding detection sensor and performing autonomous traveling,
a self-position estimation unit that estimates a position and orientation on a point cloud map as a self-position and orientation using the surrounding detection sensor;
a self-position determination unit that performs a determination process to determine whether the self-position and orientation are normal;
a driving control unit that controls the autonomous driving using the self-position and attitude when the self-position and attitude are determined to be normal,
The self-position estimation unit
If it is determined that the self-position and attitude is not normal, an acquisition process is carried out to acquire a normal self-position and attitude, and the acquisition process is repeated until the self-position and attitude obtained by the acquisition process is determined to be normal by the determination process.

In the autonomous driving mobile body according to the present disclosure, if it is determined that the self-position and attitude obtained by the process of estimating the self-position and attitude on a point cloud map using a periphery detection sensor is not normal, a different method, called acquisition processing, is used to acquire a normal self-position and attitude. Then, in the autonomous driving mobile body according to the present disclosure, the acquisition processing is repeated until the self-position and attitude obtained by the acquisition processing is determined to be normal by a determination processing. Then, in the autonomous driving mobile body according to the present disclosure, if the self-position and attitude obtained by the acquisition processing is determined to be normal, it controls autonomous driving using the self-position and attitude determined to be normal, and returns to the process of estimating the self-position and attitude on a point cloud map using a periphery detection sensor. Therefore, the autonomous driving mobile body according to the present disclosure can perform driving based on autonomous driving using a process of estimating the self-position and attitude on a point cloud map using a periphery detection sensor, achieving the effect of enabling autonomous driving with high precision and low cost.

FIG. 1 is a diagram showing an example of the appearance of an autonomous traveling vehicle according to a first embodiment. FIG. 1 is a diagram showing a configuration example of an autonomous traveling vehicle according to a first embodiment. FIG. 3 is a flowchart showing an autonomous traveling process of the autonomous traveling vehicle according to the first embodiment. FIG. 10 is a diagram showing a specific example of the first determination process according to the first embodiment. FIG. 10 is a diagram showing an example of an acquisition process according to the first embodiment. FIG. 10 is a diagram showing an example 2 of the acquisition process according to the first embodiment. FIG. 10 is a flowchart showing an autonomous traveling process of an autonomous traveling body according to a modification of the first embodiment. FIG. 10 is a diagram showing a configuration example of a control device according to a modified example of the first embodiment.

The present embodiment will now be described with reference to the drawings. In each drawing, identical or corresponding parts are designated by the same reference numerals. In describing the embodiment, the description of identical or corresponding parts will be omitted or simplified as appropriate. Furthermore, the size relationships between the components in the drawings below may differ from the actual size. Furthermore, in describing the embodiment, directions or positions such as up, down, left, right, front, rear, front and back may be indicated. These notations are used for convenience of description and do not limit the placement, direction or orientation of devices, instruments, parts, etc.

Embodiment 1.
***Configuration Description***
FIG. 1 is a diagram showing an example of the appearance of an autonomously running vehicle 10 according to this embodiment.
FIG. 2 is a diagram showing an example of the configuration of the autonomously running vehicle 10 according to this embodiment.
The autonomously traveling vehicle 10 is, for example, a vehicle such as a PMV or AMR that travels autonomously or manually on a route within a facility. The PMV is a manned vehicle. The PMV may be capable of autonomous traveling. The PMV may be capable of switching between autonomous traveling and manual traveling. The AMR is an unmanned vehicle that travels autonomously within a facility.

The autonomously traveling vehicle 10 includes a control device 100 , a GNSS receiving antenna 201 , a GNSS receiver 202 , a surroundings detection sensor 203 , and a vehicle traveling device 204 .
The control device 100 receives GNSS reception data 41 from the satellite system via a GNSS reception antenna 201 and a GNSS receiver 202 .
The control device 100 estimates its own position and orientation (localization) by SLAM using a surrounding detection sensor 203 such as the LiDAR 21, and outputs control information 31 for autonomous driving to the vehicle driving device 204. The control information 31 is information that determines the speed and steering of the autonomous driving vehicle 10.
The vehicle driving device 204 is a device such as a vehicle motor and a drive shaft, and performs autonomous driving based on the control information 31.

The autonomous vehicle 10 is equipped with a control device 100 which is a computer.
The control device 100 includes a processor 910, as well as other hardware such as a memory 921, an auxiliary storage device 922, an input interface 930, an output interface 940, a display device 941, and a communication device 950. The processor 910 is connected to the other hardware via signal lines and controls the other hardware.

The control device 100 has, as its functional elements, a self-position estimation unit 110, a self-position determination unit 120, a driving control unit 130, and a memory unit 150. The memory unit 150 stores a point cloud map 51, a self-position and attitude 52, and a position threshold 53. The point cloud map 51 is also called an environmental map. The point cloud map 51 is obtained by SLAM and is generated from three-dimensional point cloud data obtained by the periphery detection sensor 203. The point cloud map 51 may also be obtained by SLAM and generated from two-dimensional point cloud data obtained by the periphery detection sensor 203.

The functions of the self-position estimation unit 110, self-position determination unit 120, and driving control unit 130 are realized by software. The storage unit 150 is provided in the memory 921. Note that the storage unit 150 may be provided in the auxiliary storage device 922, or may be provided separately in the memory 921 and the auxiliary storage device 922.

The processor 910 is a device that executes an autonomous driving program. The autonomous driving program is a program that realizes the functions of the self-position estimation unit 110, the self-position determination unit 120, and the driving control unit 130.
The processor 910 is an IC that performs arithmetic processing. Specific examples of the processor 910 are a CPU, a DSP, and a GPU. IC is an abbreviation for Integrated Circuit. CPU is an abbreviation for Central Processing Unit. DSP is an abbreviation for Digital Signal Processor. GPU is an abbreviation for Graphics Processing Unit.

The memory 921 is a storage device that temporarily stores data. Specific examples of the memory 921 are SRAM and DRAM. SRAM is an abbreviation for Static Random Access Memory. DRAM is an abbreviation for Dynamic Random Access Memory.
The auxiliary storage device 922 is a storage device that stores data. A specific example of the auxiliary storage device 922 is an HDD. The auxiliary storage device 922 may also be a portable storage medium such as an SD (registered trademark) memory card, CF, NAND flash, a flexible disk, an optical disk, a compact disk, a Blu-ray (registered trademark) disk, or a DVD. Note that HDD is an abbreviation for Hard Disk Drive. SD (registered trademark) is an abbreviation for Secure Digital. CF is an abbreviation for CompactFlash (registered trademark). DVD is an abbreviation for Digital Versatile Disk.

The input interface 930 is a port that is connected to an input device such as a mouse, keyboard, or touch panel. Specifically, the input interface 930 is a USB terminal. Note that the input interface 930 may also be a port that is connected to a LAN. USB is an abbreviation for Universal Serial Bus. LAN is an abbreviation for Local Area Network.

The output interface 940 is a port to which a cable from an output device such as a display is connected. Specifically, the output interface 940 is a USB terminal or an HDMI (registered trademark) terminal. Specifically, the display is an LCD. The output interface 940 is also called a display interface. HDMI (registered trademark) is an abbreviation for High Definition Multimedia Interface. LCD is an abbreviation for Liquid Crystal Display.

The communication device 950 has a receiver and a transmitter. The communication device 950 is connected to a communication network such as a LAN, the Internet, a telephone line, or Wi-Fi (registered trademark). Specifically, the communication device 950 is a communication chip or NIC. NIC is an abbreviation for Network Interface Card.

The autonomous driving program is executed by the control device 100. The autonomous driving program is loaded into the processor 910 and executed by the processor 910. In addition to the autonomous driving program, the OS is also stored in the memory 921. OS is an abbreviation for Operating System. The processor 910 executes the autonomous driving program while running the OS. The autonomous driving program and OS may be stored in the auxiliary storage device 922. The autonomous driving program and OS stored in the auxiliary storage device 922 are loaded into the memory 921 and executed by the processor 910. Note that part or all of the autonomous driving program may be incorporated into the OS.

The control device 100 may be equipped with multiple processors that replace the processor 910. These multiple processors share the execution of the autonomous driving program. Each processor is a device that executes the autonomous driving program, just like the processor 910.

Data, information, signal values, and variable values used, processed, or output by the autonomous driving program are stored in memory 921, auxiliary storage device 922, or registers or cache memory within processor 910.

The "parts" of the self-location estimation unit 110, the self-location determination unit 120, and the driving control unit 130 may be read as "circuits,""steps,""procedures,""processing," or "circuitry." The autonomous driving program causes a computer to execute a self-location estimation process, a self-location determination process, and a driving control process. The "processing" of the self-location estimation process, the self-location determination process, and the driving control process may be read as a "program,""programproduct,""a computer-readable storage medium storing a program," or "a computer-readable recording medium recording a program." Furthermore, the autonomous driving method is a method performed by the control device 100 executing the autonomous driving program.
The autonomous driving program may be provided by being stored on a computer-readable recording medium. Alternatively, the autonomous driving program may be provided as a program product.

***Explanation of Operation***
Next, the operation of the autonomously traveling vehicle 10 according to this embodiment will be described. The operation procedure of the autonomously traveling vehicle 10 corresponds to an autonomous traveling method. Furthermore, a program that realizes the autonomous traveling process, which is the operation of the autonomously traveling vehicle 10, corresponds to an autonomous traveling program.

FIG. 3 is a flow diagram showing the autonomous driving process of the autonomous driving vehicle 10 according to this embodiment.
The autonomously traveling vehicle 10 is equipped with a periphery detection sensor 203 and travels autonomously. The autonomously traveling vehicle 10 uses the periphery detection sensor 203 to estimate the position and orientation in the point cloud map 51 as its own position and orientation. The autonomously traveling vehicle 10 then generates control information 31 for autonomous traveling using the estimated position and orientation.
The autonomously running vehicle 10 according to this embodiment performs autonomous running based on a technology that uses a perimeter detection sensor to estimate the position and orientation on a point cloud map as its own position and orientation.

In this embodiment, the autonomously traveling vehicle 10 is equipped with a LiDAR 21 as the periphery detection sensor 203.
The autonomous vehicle 10 estimates its own position and orientation through scan matching using the LiDAR 21. In other words, the autonomous vehicle 10 is a vehicle that implements LiDAR-SLAM. LiDAR-SLAM is a technology that can create a point cloud map and estimate its own position and orientation without relying on a satellite system such as GNSS.
The technology for estimating the vehicle's position and orientation on a point cloud map using a perimeter detection sensor is not limited to LiDAR-SLAM. This embodiment can also be applied to autonomous vehicles based on technologies such as visual SLAM, which uses a camera as a perimeter detection sensor, visual odometry, which estimates orientation using continuous images obtained from a camera, visual-inertial odometry, which estimates orientation by combining visual odometry with an inertial measurement unit (IMU), or odometer SLAM, which uses an odometer.
In addition, the autonomous vehicle 10 may perform SLAM using a stereo camera or a depth camera instead of the LiDAR 21 as the surroundings detection sensor 203 to create a point cloud map and estimate its own position and orientation.
In addition, the autonomous vehicle 10 may perform SLAM using a high-resolution millimeter-wave radar or imaging radar and a monocular camera instead of the LiDAR 21 as the surrounding detection sensor 203, to create a point cloud map and estimate its own position and orientation.
In addition, the autonomous vehicle 10 may use a fusion sensor that combines two or more of the following (11) to (14) as the surrounding detection sensor 203 to create a point cloud map and estimate its own position and orientation.
(11) LiDAR21
(12) Millimeter wave radar or imaging radar and monocular camera (13) Monocular camera (14) Stereo camera or depth camera This fusion sensor may also be combined with an electronic compass or an inertial measurement unit.

<Self-Location Estimation Process (SLAM Self-Location Estimation Process): Step S101>
In step S101, the self-location estimation unit 110 estimates the position and orientation on the point cloud map 51 as the self-location and orientation using the surrounding detection sensor 203. Specifically, the self-location estimation unit 110 estimates the self-location and orientation using LiDAR-SLAM technology. The self-location estimation unit 110 estimates the self-location and orientation by scan matching the point cloud data acquired by the LiDAR 21 with the point cloud map 51.

<Self-Position Determination Process (First Determination Process S10): Steps S102 to S103>
In step S102, the self-position determining unit 120 performs a first determination process S10 to determine whether the self-position and orientation are normal.
The self-position determination unit 120 compares the current self-position and orientation with the previously estimated self-position and orientation. The current self-position and orientation is the self-position and orientation obtained by scan matching in step S101. The previously estimated self-position and orientation is assumed to be stored in the storage unit 150 as the self-position and orientation 52.
The comparison result between the current self-position and posture and the previously estimated self-position and posture is specifically the difference in position between the current self-position and posture and the previously estimated self-position and posture.

If the position difference obtained as a result of the comparison is smaller than the position threshold value 53, the self-position determining unit 120 determines that the current self-position and orientation is normal. The self-position determining unit 120 stores the current self-position and orientation determined to be normal in the storage unit 150 as the self-position and orientation 52.
If the position difference obtained as a result of the comparison is greater than the position threshold value 53, the self-position determining unit 120 determines that the current self-position and orientation is not normal.

FIG. 4 is a diagram showing a specific example of the first determination process S10 according to this embodiment.
4 shows the self-position and orientation when the self-position estimation SLAM is performed. The self-position and orientation when the self-position estimation SLAM is performed is stored in the storage unit 150 as the self-position and orientation 52.
4 indicates the next frame, i.e., the current self-position and orientation. The dotted line PMV in the right diagram of FIG.
If the comparison result is equal to or greater than the position threshold value 53, it is determined that the "self-position is lost."
The comparison result is expressed as, for example, a value obtained by calculating an evaluation function. The comparison result is also called an estimation result. Specifically, the value obtained by calculating an evaluation function is expressed as a value such as a difference in coordinates or a difference in speed.

In this way, by determining whether the self-position and orientation are normal, it is possible to detect "self-position errors" caused by scan matching on similar terrain.
Furthermore, when there are not enough feature points and self-location estimation by scan matching is not possible, it becomes possible to detect self-location loss.

If the self-position and orientation are determined to be normal (YES in step S103), the process proceeds to step S107.
If it is determined that the self-position and orientation are not normal (NO in step S103), the process proceeds to step S104.

<Self-Location Estimation Process (Acquisition Process S30) and Self-Location Determination Process (Second Determination Process S20): Steps S104 to S106>
If the self-position estimation unit 110 determines in step S104 that the self-position and orientation are not normal, it performs an acquisition process S30 to acquire a normal self-position and orientation. Note that the acquisition process S30 is repeated until the self-position and orientation obtained by the acquisition process S30 is determined to be normal by the second determination process S20.
Specifically, when the self-position estimation unit 110 determines that the self-position and attitude are not normal, the self-position estimation unit 110 acquires the current self-position and attitude using the GNSS reception data 41 received by the GNSS receiver 202 as an acquisition process S30.

FIG. 5 is a diagram showing an example of the acquisition process S30 according to this embodiment.
If the self-position estimation unit 110 determines that the self-position and attitude are incorrect, it performs an acquisition process S30 in which it acquires the current self-position and attitude using the GNSS reception data 41 received by the GNSS receiver 202. That is, in the acquisition process S30, the current self-position and attitude are estimated using the GNSS receiver 202, which is a device different from the device used to estimate the self-position and attitude by SLAM using the periphery detection sensor 203. This attitude estimation will be described later in examples 1 to 5 of the acquisition process S30. Note that the different device in this case may be a camera and an image processor for the camera image, or other devices. Examples of other devices may include millimeter-wave radar or imaging radar and a camera, an electronic compass, an inertial measurement unit, etc.

The upper left diagram of FIG. 5, like the right diagram of FIG. 4, shows a state in which the comparison result is equal to or greater than the position threshold value and it is determined that "self-position lost."
When a self-position loss is detected, the self-position estimation unit 110 estimates the self-position and orientation using the GNSS reception data 41. Note that the self-position estimation is not always performed using the GNSS reception data 41, but is performed only when a self-position loss is detected.
As shown in the upper right diagram of FIG. 5, the self-position estimation unit 110 estimates the self-position and attitude using the GNSS reception data 41, and sets the self-position and attitude based on the GNSS reception data 41 as the current self-position and attitude.

In step S105, the self-position determining unit 120 performs a second determination process S20 to determine whether the self-position and orientation obtained in the acquisition process S30 are normal or not.
The second determination process S20 is performed in the same manner as the first determination process S10.
In the first determination process S10, the current self-position and attitude to be compared with the previously estimated self-position and attitude is the self-position and attitude acquired by SLAM in step S101. On the other hand, in the second determination process S20, the current self-position and attitude to be compared with the previously estimated self-position and attitude is the self-position and attitude acquired by the acquisition process S30 in step S104. For example, the current self-position and attitude to be compared with the previously estimated self-position and attitude is the self-position and attitude acquired from the GNSS reception data 41 in step S104.

If the position difference obtained as a result of the comparison is smaller than the position threshold value 53, the self-position determination unit 120 determines that the current self-position and attitude is normal. The self-position determination unit 120 may store the current self-position and attitude determined to be normal in the storage unit 150 as the self-position and attitude 52. The final normal self-position and attitude is the "self-position and attitude obtained by SLAM." In step S106 of FIG. 3, the "self-position and attitude obtained from the GNSS reception data 41" is equal to the "self-position and attitude obtained by LiDAR-SLAM," but the self-position and attitude in this case is always the "self-position and attitude obtained by LiDAR-SLAM."
If the position difference obtained as a result of the comparison is greater than the position threshold value 53, the self-position determining unit 120 determines that the current self-position and orientation is not normal.

As shown in the lower diagram of Figure 5, the self-position determination unit 120 determines that the current self-position and attitude is normal, taking the self-position and attitude based on the GNSS reception data 41 as the current self-position and attitude.

If the self-position and orientation are determined to be normal (YES in step S106), the process proceeds to step S107.
If it is determined that the self-position and orientation are not normal (NO in step S106), the process returns to step S104 and the acquisition process S30 is repeated.

<Driving Control Processing: Step S107>
In step S107, the driving control unit 130 controls the autonomous driving using the self-position and attitude determined to be normal. The driving control unit 130 generates control information 31 for controlling the autonomous driving using the self-position and attitude determined to be normal, and outputs the control information 31 to the vehicle driving device 204. The control information 31 is information that determines the speed and steering of the autonomously driving vehicle 10. The vehicle driving device 204 is a device such as a vehicle motor and drive shaft, and performs autonomous driving based on the control information 31.

The driving control unit 130 may decelerate the autonomous driving until the second determination process S20 determines that the self-position and attitude are correct. Alternatively, the driving control unit 130 may stop the autonomous driving until the second determination process S20 determines that the self-position and attitude are correct.

Here, we will explain other examples of the acquisition process S30 in this embodiment.

<Example 1 of Acquisition Process S30>
When the self-position estimation unit 110 determines that the self-position and attitude are incorrect, in the acquisition process S30, the self-position estimation unit 110 may acquire the current self-position using the GNSS reception data 41 and may acquire the attitude of the previously estimated self-position and attitude as the current attitude. When the current self-position is acquired using the GNSS reception data 41, the position may be estimated by high-precision RTK (Real Time Kinematic) positioning with a position error of the order of centimeters.

<Example 2 of Acquisition Process S30>
FIG. 6 is a diagram showing a second example of the acquisition process S30 according to the present embodiment.
If the self-position estimation unit 110 determines that the self-position and attitude are incorrect, in acquisition process S30, it acquires the current self-position and attitude using the GNSS reception data 41. When repeating acquisition process S30, the self-position estimation unit 110 may set a position obtained by gradually shifting the self-position of the self-position and attitude as the current self-position, and may acquire an attitude shifted by a predetermined angle from the attitude of the self-position and attitude as the current attitude.
Then, the self-position estimation unit 110 sets the current self-position and current attitude as the current self-position and attitude, and performs the second determination process S20 and subsequent processes.
Here, the predetermined angle is, for example, 10 degrees. If the predetermined angle is 1 degree, the acquisition process S30 will be performed a maximum of 360 times until the current self-position and orientation is determined to be normal.

<Example 3 of acquisition process S30>
When the self-position estimation unit 110 determines that the self-position and attitude are incorrect, it may acquire the current self-position using the GNSS reception data 41 and the current attitude using an electronic compass as an acquisition process S30.
Furthermore, when the self-position estimation unit 110 determines that the self-position and attitude are incorrect, as an acquisition process S30, it may acquire the current self-position using the GNSS reception data 41 and also acquire the current attitude using an inertial measurement device.
Furthermore, when the self-position estimation unit 110 determines that the self-position and attitude are incorrect, as an acquisition process S30, it may acquire the current self-position using the GNSS reception data 41, and may also use the previous self-position acquired using the previous GNSS reception data 41 to acquire the vector of the difference between the current self-position using the GNSS reception data 41 and the previous self-position, and the current attitude using an inertial measurement unit.

<Example 4 of Acquisition Process S30>
If the self-position estimation unit 110 determines that the self-position and orientation are incorrect, the self-position estimation unit 110 may acquire the current self-position and orientation from road landmarks or the like using a camera in acquisition processing S30.

<Example 5 of Acquisition Process S30>
As described above, the acquisition process S30 is repeated until the self-position and orientation obtained by the acquisition process S30 is determined to be normal by the second determination process S20.
At this time, for example, the acquisition process S30 may be repeated by determining rules such as those in (1) to (3) below.
(1) From 1st to Kth time: The self-position and attitude are acquired using the GNSS reception data 41.
(2) From K+1 to L times: The self-position is acquired using the GNSS reception data 41.
The attitude is acquired from the attitude of the previous self-position and attitude stored in the storage unit 150. (3) From L+1 to Mth time The self-position is acquired using the GNSS reception data 41.
The attitude is obtained using an electronic compass.

The numbers K, L, and M are any integers that satisfy the relationship 1<K<L<M, for example.
The order of (1) to (3) and the combination of methods for obtaining the self-position and attitude are also arbitrary.

A count threshold may be stored in the storage unit 150. The self-position estimation unit 110 may have a function of issuing a warning message indicating that a normal self-position and orientation cannot be acquired when the number of times the acquisition process S30 is repeated exceeds the count threshold.
Alternatively, the time threshold may be stored in the storage unit 150. The self-position estimation unit 110 may have a function of issuing a warning message indicating that a normal self-position and orientation cannot be acquired when the time taken to repeat the acquisition process S30 exceeds the time threshold.

***Other configurations***
<Modification 1>
In this embodiment, an aspect of autonomous driving based on a method of estimating self-position and orientation using LiDAR-SLAM has been described.
As a modified example, the autonomously traveling vehicle may be equipped with a camera and may travel autonomously based on a method of estimating its own position and orientation using visual SLAM using the camera.
Alternatively, the autonomously traveling vehicle may be equipped with an odometer and may travel autonomously based on a method of estimating its own position and orientation using odometer SLAM (Simultaneous Local Area Measurement) that uses the odometer.

<Modification 2>
In this embodiment, after acquiring the vehicle's position and attitude using GNSS reception data, if the acquired position and attitude is normal, the vehicle performs driving control using the position and attitude (based on the GNSS reception data).Then, the vehicle's position and attitude is estimated using LiDAR-SLAM, which serves as the base.
On the other hand, in this modified example, after obtaining the self-position and attitude from the GNSS reception data, if the obtained self-position and attitude is normal, the self-position and attitude may be estimated again using LiDAR-SLAM.

FIG. 7 is a flowchart showing the autonomous driving process of the autonomous driving vehicle 10 according to a modified example of this embodiment.
7 differs from FIG. 3 in that if the answer to step S106 is YES, the process returns to step S101 and the self-position and orientation are estimated using LiDAR-SLAM as the base.
Specifically, in FIG. 7, the following four processes can be imagined to be performed.
(1) LiDAR-SLAM makes the vehicle's position unstable.
(2) The vehicle's own position from the GNSS receiver is "temporarily stored in memory."
(3) The information in (2) is transmitted as complementary information to the LiDAR-SLAM self-position estimation process.
(4) Once the vehicle's position is stabilized by LiDAR-SLAM self-location estimation, autonomous driving resumes. The information from (2) is deleted from memory.

Note that <Example 2 of acquisition process S30> described in the first embodiment may be implemented in the process of Fig. 7. <Example 2 of acquisition process S30> is a process when the self-position is roughly correct but the posture is not.
When the self-position estimation unit 110 determines that the self-position and attitude are incorrect (step S103), the self-position estimation unit 110 acquires the current self-position and attitude using the GNSS reception data 41 as the acquisition process S30 (step S104). When repeating the acquisition process S30, the self-position estimation unit 110 may set a position obtained by gradually shifting the self-position of the self-position and attitude as the current self-position, and acquire an attitude shifted by a predetermined angle from the attitude of the self-position and attitude as the current attitude (step S104).
Then, the self-position estimation unit 110 sets the current self-position and current attitude as the current self-position and attitude, and performs the second determination process S20 and subsequent processes (step S105).
In step S106, if the current self-position and orientation is normal, the process returns to step S101 and the LiDAR-SLAM self-position estimation process is performed.

As described above, even if LiDAR-SLAM detects that the vehicle has lost its position, it is possible to return to LiDAR-SLAM by obtaining its own position and attitude from GNSS reception data.
Furthermore, since the final estimation of the vehicle's position and attitude is performed using LiDAR-SLAM, it is possible to estimate the vehicle's position and attitude even if the GNSS receiver is not fixed.

<Modification 3>
In this embodiment, the functions of the self-location estimation unit 110, the self-location determination unit 120, and the driving control unit 130 are realized by software. As a modified example, the functions of the self-location estimation unit 110, the self-location determination unit 120, and the driving control unit 130 may be realized by hardware.
Specifically, the control device 100 includes an electronic circuit 909 instead of a processor 910 .

FIG. 8 is a diagram showing an example of the configuration of a control device 100 according to a modification of this embodiment.
The electronic circuit 909 is a dedicated electronic circuit that realizes the functions of the self-position estimation unit 110, the self-position determination unit 120, and the driving control unit 130. Specifically, the electronic circuit 909 is a single circuit, a composite circuit, a programmed processor, a parallel programmed processor, a logic IC, a GA, an ASIC, or an FPGA. GA is an abbreviation for Gate Array. ASIC is an abbreviation for Application Specific Integrated Circuit. FPGA is an abbreviation for Field-Programmable Gate Array.

The functions of the self-position estimation unit 110, self-position determination unit 120, and driving control unit 130 may be realized by a single electronic circuit, or may be distributed across multiple electronic circuits.

As another variation, some of the functions of the self-position estimation unit 110, self-position determination unit 120, and driving control unit 130 may be implemented by electronic circuits, with the remaining functions being implemented by software. Also, some or all of the functions of the self-position estimation unit 110, self-position determination unit 120, and driving control unit 130 may be implemented by firmware.

Each of the processor and electronic circuits is also called processing circuitry. In other words, the functions of the self-position estimation unit 110, self-position determination unit 120, and driving control unit 130 are realized by processing circuitry.

***Description of the Effects of This Embodiment***
In the autonomously running mobile body according to this embodiment, if it is determined that the self-position and attitude obtained by the process of estimating the self-position and attitude by SLAM using a periphery detection sensor such as LiDAR is not normal, a normal self-position and attitude is obtained by an acquisition process, which is a different method. Then, in the autonomously running mobile body according to this embodiment, the acquisition process is repeated until the self-position and attitude obtained by the acquisition process is determined to be normal by a determination process. In the autonomously running mobile body according to this embodiment, if the self-position and attitude obtained by the acquisition process is determined to be normal, the autonomously running mobile body controls autonomous running using the self-position and attitude determined to be normal, and autonomously runs by estimating the self-position and attitude by SLAM using a periphery detection sensor.

SLAM, or scan matching, using a perimeter detection sensor provides highly accurate self-position and orientation. However, the vehicle may lose track of its own position if it rotates or if the approximate location and the entire surrounding field of view are obscured. In such cases, the present embodiment aims to restore scan matching on the spot, rather than moving the autonomous vehicle using an alternative method other than scan matching.
Therefore, according to the autonomously driving vehicle of this embodiment, it is possible to continue driving based on autonomous driving using SLAM with a surrounding detection sensor, thereby achieving the effect of enabling autonomous driving to be performed with high precision and low cost.

In the first embodiment described above, each unit of the control device has been described as an independent functional block. However, the configuration of the control device does not have to be the same as that of the above-described embodiment. The functional blocks of the control device may have any configuration as long as they can realize the functions described in the above-described embodiment. Furthermore, the control device may not be a single device, but may be a system composed of multiple devices.
Furthermore, it is possible to combine multiple parts of the first embodiment and implement it. Alternatively, it is possible to implement only one part of the first embodiment. In addition, it is possible to combine the first embodiment in whole or in part in any way.
That is, in the first embodiment, it is possible to freely combine parts of the first embodiment, or to modify any of the components of the first embodiment, or to omit any of the components of the first embodiment.

Note that the above-described embodiments are essentially preferred examples and are not intended to limit the scope of the present disclosure, the scope of applications of the present disclosure, or the scope of uses of the present disclosure. The above-described embodiments can be modified in various ways as needed. For example, procedures explained using flow charts or sequence diagrams may be modified as appropriate.

10 Autonomous driving vehicle, 21 LiDAR, 31 Control information, 41 GNSS reception data, 51 Point cloud map, 52 Self-position and attitude, 53 Position threshold, 100 Control device, 110 Self-position estimation unit, 120 Self-position determination unit, 130 Driving control unit, 150 Memory unit, 201 GNSS receiving antenna, 202 GNSS receiver, 203 Surrounding detection sensor, 204 Vehicle driving device, 909 Electronic circuit, 910 Processor, 921 Memory, 922 Auxiliary storage device, 930 Input interface, 940 Output interface, 941 Display device, 950 Communication device, S10 First determination process, S20 Second determination process, S30 Acquisition process.

Claims (17)

In an autonomous vehicle equipped with a surrounding detection sensor and capable of autonomous driving,
a self-position estimation unit that estimates a position and orientation on a point cloud map as a self-position and orientation using the surrounding detection sensor;
a self-position determination unit that performs a determination process to determine whether the self-position and orientation are normal;
a driving control unit that controls the autonomous driving using the self-position and attitude when the self-position and attitude are determined to be normal,
The self-position estimation unit
When it is determined that the self-position and attitude is not normal, an acquisition process is carried out to acquire a normal self-position and attitude, and the acquisition process is repeated until the self-position and attitude obtained by the acquisition process is determined to be normal by the determination process. The autonomous traveling vehicle is equipped with a LiDAR,
The self-position estimation unit
The autonomous vehicle according to claim 1 , wherein the self-position and orientation are estimated by scan matching using the LiDAR. The self-position estimation unit
3. The autonomous vehicle according to claim 1, wherein, when the number of times the acquisition process is repeated exceeds a threshold number, a warning message is sent to indicate that a normal self-position and orientation cannot be acquired. The self-position estimation unit
3. The autonomous vehicle according to claim 1, wherein, when a time period during which the acquisition process is repeated exceeds a time threshold, a warning message is sent to indicate that a normal self-position and orientation cannot be acquired. The self-position estimation unit
The previously estimated self-position and orientation are stored in a memory unit.
The self-position determination unit
5. The autonomously traveling vehicle according to claim 1, wherein the current self-position and attitude are compared with the previously estimated self-position and attitude, and if the comparison result is smaller than a position threshold, the current self-position and attitude are determined to be normal, and if the comparison result is larger than the position threshold, the current self-position and attitude are determined to be abnormal. The self-position estimation unit
6. An autonomous vehicle according to claim 1, wherein when it is determined that the self-position and attitude are incorrect, the acquisition process acquires the current self-position and attitude by position estimation using equipment other than localization using the surrounding detection sensor. The autonomous traveling vehicle includes a GNSS (Global Navigation Satellite System) receiver,
The self-position estimation unit
An autonomously moving vehicle as described in any one of claims 1 to 5, wherein when it is determined that the self-position and attitude are incorrect, the acquisition process acquires the current self-position and attitude using GNSS reception data received by the GNSS receiver. the autonomously traveling vehicle includes a GNSS receiver;
The self-position estimation unit
6. The autonomously moving vehicle of claim 5, wherein, when it is determined that the self-position and attitude are incorrect, the acquisition process involves acquiring the current self-position using GNSS reception data received by the GNSS receiver, and acquiring the attitude of the previously estimated self-position and attitude as the current attitude. the autonomously traveling vehicle includes a GNSS receiver;
The self-position estimation unit
An autonomously driving vehicle as described in any one of claims 1 to 5, wherein when it is determined that the self-position and attitude are incorrect, the acquisition process involves acquiring the current self-position using GNSS reception data received by the GNSS receiver, and acquiring the current attitude using an electronic compass. the autonomously traveling vehicle includes a GNSS receiver;
The self-position estimation unit
An autonomously moving vehicle as described in any one of claims 1 to 5, wherein when it is determined that the self-position and attitude are incorrect, the acquisition process involves acquiring the current self-position using GNSS reception data received by the GNSS receiver and acquiring the current attitude using an inertial measurement device. the autonomously traveling vehicle includes a GNSS receiver;
The self-position estimation unit
6. An autonomously running vehicle as described in any one of claims 1 to 5, wherein, when it is determined that the self-position and attitude are incorrect, the acquisition process involves acquiring the current self-position using GNSS reception data received by the GNSS receiver, and acquiring an attitude shifted by a predetermined angle from the attitude of the self-position and attitude as the current attitude. The traveling control unit
The autonomously traveling vehicle according to claim 1 , wherein the autonomously traveling vehicle is decelerated until the self-position and attitude are determined to be correct by the determination process. The traveling control unit
The autonomously running vehicle according to claim 1 , wherein the autonomously running vehicle stops autonomously running until the self-position and attitude are determined to be correct by the determination process. the autonomously traveling vehicle is equipped with a camera,
The self-position estimation unit
The autonomous vehicle according to claim 1 , wherein the self-position and orientation are estimated by visual SLAM (Simultaneous Localization and Mapping) using the camera. the autonomous vehicle is equipped with an odometer,
The self-position estimation unit
The autonomous vehicle according to claim 1 , wherein the self-position and orientation are estimated by an odometer SLAM using the odometer. An autonomous driving method used for an autonomously driving mobile body that is equipped with a periphery detection sensor and that performs autonomous driving,
a computer using the surrounding detection sensor to estimate the position and orientation in the point cloud map as the self-position and orientation;
a computer performs a determination process to determine whether the self-position and orientation are normal;
When the computer determines that the self-position and attitude are normal, the computer controls the autonomous traveling using the self-position and attitude;
An autonomous driving method characterized in that, when a computer determines that the self-position and attitude is not normal, it performs an acquisition process to acquire a normal self-position and attitude, and repeats the acquisition process until the self-position and attitude obtained by the acquisition process is determined to be normal by the determination process. An autonomous driving program used in a computer mounted on an autonomously driving vehicle equipped with a surrounding detection sensor and capable of autonomous driving,
a self-position estimation process for estimating a position and orientation on a point cloud map as a self-position and orientation using the surrounding detection sensor;
a self-position determination process for determining whether the self-position and posture are normal;
and a driving control process for controlling the autonomous driving using the self-position and attitude when the self-position and attitude are determined to be normal,
The self-location estimation process includes:
When it is determined that the self-position and attitude is not normal, an acquisition process is carried out to acquire a normal self-position and attitude, and the acquisition process is repeated until the self-position and attitude obtained by the acquisition process is determined to be normal by the judgment process.