CN107421537A - Object motion attitude cognitive method and system based on inertial sensor rigid body grid - Google Patents

Abstract

The invention discloses a method and system for sensing the motion posture of an object based on an inertial sensor rigid body grid, which combines sensor rigid body grid positioning technology, adaptive weight distribution technology and multi-inertial sensor information fusion technology, and uses multiple inertial sensor nodes to form The peer-to-peer sensor grid network analyzes and fuses the attitude sensing data of multiple sensor nodes, and calculates the complete motion of the object attached to the grid system through the correction of the rigid body grid position error and the dynamic error correction of the grid node data. attitude. The invention can improve the perception accuracy of the motion parameters of the inertial sensor, and realize the real-time motion trajectory and accurate perception tracking of the motion posture of the moving object. The key steps include rigid body grid position error correction and grid node data dynamic error correction.

Description

Object motion attitude perception method and system based on inertial sensor rigid body grid

technical field

The present invention relates to the technical field of inertial sensors, in particular to a method and system for sensing motion and attitude of an object based on rigid body grids of inertial sensors.

Background technique

At present, inertial sensors have been widely used in mobile electronic devices. By fusing the sensing data of accelerometers, gyroscopes, and magnetometers of inertial sensors, calculating the motion posture and trajectory of objects has become the main application field of inertial sensors. There are a wide range of application requirements in the fields of positioning, aircraft position and attitude in the air, and object movement and attitude detection. However, due to the limitations of inertial sensor technology, cost, accuracy, interference, cumulative error and other factors, the motion attitude perception accuracy of inertial sensors still cannot meet the accuracy requirements of realistic attitude monitoring and trajectory tracking, thus restricting the inertial sensor attitude monitoring scheme. practicability and available scenarios.

Contents of the invention

The object of the present invention is to solve the problem that the perception accuracy of the motion posture of the existing inertial sensor cannot meet the actual requirements, and provides a method and system for sensing the motion posture of an object based on the rigid body grid of the inertial sensor.

In order to solve the above problems, the present invention is achieved through the following technical solutions:

An object motion attitude perception method based on an inertial sensor rigid body grid includes the following steps:

Step 1. On the object to be measured, use more than two inertial sensors with rigid spatial positional relationships as grid nodes to construct an inertial sensor rigid body grid network with more than two nodes and a rigid spatial structure formed between the nodes;

Step 2. Rigid body grid position error correction:

Step 2.1, allowing the measured object to perform a predetermined calibration movement;

Step 2.2, by obtaining the motion attitude data output by the inertial sensor when the object under test performs calibration movement, and combining the known grid position relationship of the inertial sensor in the inertial sensor rigid body grid network, the inertial sensor rigid body grid network is The calibration of the grid position and the correction of the grid position relationship obtain the precise position relationship model of the rigid body grid network of the inertial sensor;

Step 3. When the measured object is actually moving, each inertial sensor of the rigid body grid network of the inertial sensor on it collects and outputs corresponding motion attitude data in real time, and calculates these motion attitude data in real time to obtain each inertial sensor Preliminary estimated motion pose of the inertial sensor rigid body mesh network;

Step 4, grid node data dynamic error correction;

Step 4.1, based on the precise position relationship model of the inertial sensor rigid body grid network obtained in step 2 and the motion attitude of the inertial sensor rigid body grid network initially estimated by the inertial sensors obtained in step 3, calculate the information confidence of each inertial sensor node degree weight, and obtain the information confidence model of the inertial sensor rigid body grid network under the motion attitude;

Step 4.2, according to the movement attitude data perceived by each inertial sensor of the inertial sensor rigid body grid network, calculate the movement attitude estimation of other inertial sensors respectively;

Step 4.3, according to the information confidence model of the inertial sensor rigid body grid network obtained in step 4.1, perform iterative calculation between each grid node of the inertial sensor rigid body grid network, and obtain each network of the inertial sensor rigid body grid network Accurate attitude data of grid nodes;

Step 4.4, taking the precise attitude data of the weight center mass point of the rigid body grid network of the inertial sensor as the final perceived movement attitude of the measured object.

In the above step 1, all inertial sensor nodes constitute a peer-to-peer inertial sensor grid network.

The object movement posture perception system based on the inertial sensor rigid body grid for realizing the above method is characterized in that the system is composed of a data processing unit and more than two inertial sensor nodes with a rigid spatial position relationship;

The inertial sensor node is responsible for sensing the motion posture data, and transmits the motion posture data to the data processing unit through the communication network;

The data processing unit is responsible for dynamic analysis of the motion attitude data of each inertial sensor node, and through rigid body grid position error correction and grid node data dynamic error correction, the fusion solution is used to calculate the precise motion attitude data of each inertial sensor node.

In the above scheme, all inertial sensor nodes constitute a peer-to-peer inertial sensor rigid body grid network.

In the above solutions, the inertial sensor nodes are connected to the data processing unit through wired and/or wireless means.

Compared with the prior art, the present invention combines sensor rigid body grid positioning technology, adaptive weight distribution technology and multi-inertial sensor information fusion technology, and has the following characteristics:

1. Use a peer-to-peer sensor grid network composed of multiple inertial sensor nodes to analyze and fuse the attitude sensing data of multiple sensor nodes, so as to calculate the complete motion attitude of the object attached to the grid system;

2. Each inertial sensor node has a rigid spatial position relationship, and its spatial position relationship also becomes an influencing factor of its information confidence weight, so that the motion posture data of a single sensor can be used to estimate the motion posture of other nodes, which is the basis for other nodes. Provide a reference basis for error correction;

3. The dynamic error reference weight model is adopted. The weight model of the sensor grid network has no fixed center, and the relationship between each inertial sensor node is equal. The error reference weight of each node is determined by the motion attitude estimation of the rigid body grid system, and The spatial position relationship among them is used to jointly determine the error reference weight, and iteratively corrects continuously, and finally obtains high-precision motion attitude data;

4. Calibrate the position of the rigid body grid of the inertial sensor by using a specific object calibration movement, and correct the error correction of the grid position relationship, thereby providing an accurate and precise grid position relationship for the subsequent iterative calculation of the error model.

Description of drawings

FIG. 1 is a flow chart of an object motion attitude perception method based on an inertial sensor rigid body grid.

detailed description

In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in combination with specific examples and with reference to the accompanying drawings.

The object motion attitude perception system based on the inertial sensor rigid body grid is composed of a data processing unit and more than two inertial sensor nodes. Inertial sensor nodes constitute a peer-to-peer sensor rigid body grid network. Each inertial sensor node has a rigid spatial position relationship, and each grid node has a non-central peer relationship, so that the motion attitude data of a single inertial sensor can be , to estimate the motion attitude of other nodes, and provide a reference for error correction of other nodes. The inertial sensor node is responsible for sensing the motion posture data (acceleration, angular velocity, magnetic field vector, etc.), and transmits the motion posture data to the data processing unit through the communication network. The data processing unit is responsible for dynamic analysis of the motion attitude data of each inertial sensor node, and through rigid body grid position error correction and grid node data dynamic error correction, the fusion solution calculates the precise motion attitude data of each inertial sensor node.

The object motion attitude perception method based on the inertial sensor rigid body grid implemented by the above system is shown in Figure 1, and its specific steps are as follows:

Step 1. On the object to be measured, use more than two inertial sensors with rigid spatial positional relationships as grid nodes to construct an inertial sensor rigid body grid network with more than two nodes and a rigid spatial structure formed between the nodes;

Step 2. Rigid body grid position error correction:

Step 2.1, allowing the measured object to perform a predetermined calibration movement;

Step 2.2, by obtaining the motion attitude data output by the inertial sensor when the object under test performs calibration movement, and combining the known grid position relationship of the inertial sensor in the inertial sensor rigid body grid network, the inertial sensor rigid body grid network is The calibration of the grid position and the correction of the grid position relationship obtain the precise position relationship model of the rigid body grid network of the inertial sensor;

Step 3. When the measured object is actually moving, each inertial sensor of the rigid body grid network of the inertial sensor on it collects and outputs corresponding motion attitude data in real time, and calculates these motion attitude data in real time to obtain each inertial sensor Preliminary estimated motion pose of the inertial sensor rigid body mesh network;

Step 4, grid node data dynamic error correction;

Step 4.1, based on the precise position relationship model of the inertial sensor rigid body grid network obtained in step 2 and the motion attitude of the inertial sensor rigid body grid network initially estimated by the inertial sensors obtained in step 3, calculate the information confidence of each inertial sensor node degree weight, and obtain the information confidence model of the inertial sensor rigid body grid network under the motion attitude;

Step 4.2, according to the movement attitude data perceived by each inertial sensor of the inertial sensor rigid body grid network, calculate the movement attitude estimation of other inertial sensors respectively;

Step 4.3, according to the information confidence model of the inertial sensor rigid body grid network obtained in step 4.1, perform iterative calculation between each grid node of the inertial sensor rigid body grid network, and obtain each network of the inertial sensor rigid body grid network Accurate attitude data of grid nodes;

Step 4.4, taking the precise attitude data of the weight center mass point of the rigid body grid network of the inertial sensor as the final perceived movement attitude of the measured object.

The present invention uses a plurality of inertial sensors with rigid spatial position relationship as grid nodes on the rigid object to construct a sensor grid system with multiple multi-nodes and a rigid spatial structure formed between the nodes, so as to perform the precise attitude of the rigid object data calculation. Through the real-time calculation of multi-inertial sensor data, the preliminary motion attitude estimation of the rigid body grid system is formed; according to the current preliminary motion attitude estimation parameters, the information confidence weight of each node is dynamically allocated, and the reference weight model of the attitude error is calculated. Through the reference weight model of each inertial sensor error, combined with the known spatial position of the rigid body grid, the iterative correction of the error between multiple sensors is performed. In this way, the dynamic and accurate correction of the motion attitude perception data of each node of the sensor grid is realized, and the precise attitude data of each grid node and the center of the grid system is calculated. The invention can improve the perception accuracy of the motion parameters of the inertial sensor, and realize the real-time motion trajectory and accurate perception tracking of the motion posture of the moving object. The key steps include rigid body grid position error correction and grid node data dynamic error correction.

Rigid body grid position error correction: An inertial sensor grid system with a rigid body position relationship is placed on the measured object to make the object perform a specific calibration movement, such as horizontal stillness, free fall movement, etc., by obtaining the inertia of the object during calibration movement The motion attitude output data of the sensor grid node, combined with the known grid position relationship of the inertial sensor, is used to calibrate the grid position and correct the grid position relationship, so as to obtain the rigid body grid of the inertial sensor after error correction The precise positional relationship of the rigid body provides an accurate rigid body positional relationship model for subsequent attitude data correction.

Grid node data dynamic error correction: When the measured object is moving, each sensor node of the inertial sensor rigid body grid will collect and output the corresponding motion attitude data (acceleration, angular velocity, magnetic field vector, etc.) in real time. It is transmitted to the data processing unit in a wired or wireless manner, and the overall preliminary estimation of the motion attitude of the rigid body grid is performed, so as to obtain the overall motion data such as the motion direction, speed, and rotation angle of the center point of the rigid body grid. Combined with the precise position relationship model of the grid, the information confidence weight of each grid node is calculated, so as to obtain the information confidence model of the grid node under the motion posture. On this basis, according to the motion attitude perception data of the inertial sensor grid node, the motion attitude estimation of other nodes is calculated respectively, and the error iterative calculation between each node is carried out according to the information confidence model of the grid node, and the obtained Error correction model for all mesh nodes. According to the error correction model, the motion attitude data of each grid node is corrected by error compensation, so as to obtain the precise motion attitude data of each grid node, and calculate the precise attitude data of the weight center particle of the grid system.

It should be noted that although the above embodiments of the present invention are illustrative, they are not intended to limit the present invention, so the present invention is not limited to the above specific implementation manners. Without departing from the principles of the present invention, all other implementations obtained by those skilled in the art under the inspiration of the present invention are deemed to be within the protection of the present invention.

Claims (5)

1. The object movement posture perception method based on inertial sensor rigid body grid is characterized in that, comprises the steps: Step 1. On the object to be measured, use more than two inertial sensors with rigid spatial positional relationships as grid nodes to construct an inertial sensor rigid body grid network with more than two nodes and a rigid spatial structure formed between the nodes; Step 2. Rigid body grid position error correction: Step 2.1, allowing the measured object to perform a predetermined calibration movement; Step 2.2, by obtaining the motion attitude data output by the inertial sensor when the object under test performs calibration movement, and combining the known grid position relationship of the inertial sensor in the inertial sensor rigid body grid network, the inertial sensor rigid body grid network is The calibration of the grid position and the correction of the grid position relationship obtain the precise position relationship model of the rigid body grid network of the inertial sensor; Step 3. When the measured object is actually moving, each inertial sensor of the rigid body grid network of the inertial sensor on it collects and outputs corresponding motion attitude data in real time, and calculates these motion attitude data in real time to obtain each inertial sensor Preliminary estimated motion pose of the inertial sensor rigid body mesh network; Step 4, grid node data dynamic error correction; Step 4.1, based on the precise position relationship model of the inertial sensor rigid body grid network obtained in step 2 and the motion attitude of the inertial sensor rigid body grid network initially estimated by the inertial sensors obtained in step 3, calculate the information confidence of each inertial sensor node degree weight, and obtain the information confidence model of the inertial sensor rigid body grid network under the motion attitude; Step 4.2, according to the movement attitude data perceived by each inertial sensor of the inertial sensor rigid body grid network, calculate the movement attitude estimation of other inertial sensors respectively; Step 4.3, according to the information confidence model of the inertial sensor rigid body grid network obtained in step 4.1, perform iterative calculation between each grid node of the inertial sensor rigid body grid network, and obtain each network of the inertial sensor rigid body grid network Accurate attitude data of grid nodes; Step 4.4, taking the precise attitude data of the weight center mass point of the rigid body grid network of the inertial sensor as the final perceived movement attitude of the measured object. 2. The object movement posture perception method based on inertial sensor rigid body grid according to claim 1, characterized in that, in step 1, what all inertial sensor nodes constitute is a peer-to-peer inertial sensor grid network. 3. realize the object movement posture perception system based on inertial sensor rigid body grid of method described in claim 1, it is characterized in that, this system is made up of data processing unit and more than 2 inertial sensor nodes with rigid space position relation; The inertial sensor node is responsible for sensing the motion posture data, and transmits the motion posture data to the data processing unit through the communication network; The data processing unit is responsible for dynamic analysis of the motion attitude data of each inertial sensor node, and through rigid body grid position error correction and grid node data dynamic error correction, the fusion solution is used to calculate the precise motion attitude data of each inertial sensor node. 4. The object motion attitude perception system based on inertial sensor rigid body grid according to claim 3, wherein all inertial sensor nodes form a peer-to-peer inertial sensor rigid body grid network. 5. The object motion attitude perception system based on inertial sensor rigid body grid according to claim 3, wherein the inertial sensor node is connected with the data processing unit by wired and/or wireless means.