Yuan et al., 2020 - Google Patents
RGGNet: Tolerance aware LiDAR-camera online calibration with geometric deep learning and generative modelYuan et al., 2020
- Document ID
- 5263035734223411627
- Author
- Yuan K
- Guo Z
- Wang Z
- Publication year
- Publication venue
- IEEE Robotics and Automation Letters
External Links
Snippet
Accurate LiDAR-camera online calibration is critical for modern autonomous vehicles and robot platforms. Dominant methods heavily rely on hand-crafted features, which are not scalable in practice. With the increasing popularity of deep learning (DL), a few recent efforts …
- 239000000203 mixture 0 abstract description 10
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6201—Matching; Proximity measures
- G06K9/6202—Comparing pixel values or logical combinations thereof, or feature values having positional relevance, e.g. template matching
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
- G06K9/46—Extraction of features or characteristics of the image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/20—Analysis of motion
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/00624—Recognising scenes, i.e. recognition of a whole field of perception; recognising scene-specific objects
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Yuan et al. | RGGNet: Tolerance aware LiDAR-camera online calibration with geometric deep learning and generative model | |
| Chen et al. | Deep learning for visual localization and mapping: A survey | |
| Chen et al. | A survey on deep learning for localization and mapping: Towards the age of spatial machine intelligence | |
| Sahu et al. | Artificial intelligence (AI) in augmented reality (AR)-assisted manufacturing applications: a review | |
| Zeng et al. | 3dmatch: Learning local geometric descriptors from rgb-d reconstructions | |
| Ye et al. | Keypoint-based LiDAR-camera online calibration with robust geometric network | |
| Zhang et al. | Review of visual simultaneous localization and mapping based on deep learning | |
| Shen et al. | YCANet: Target detection for complex traffic scenes based on camera-LiDAR fusion | |
| Wang et al. | MCF3D: Multi-stage complementary fusion for multi-sensor 3D object detection | |
| Lee et al. | $^{2} $: LiDAR-camera loop constraints for cross-modal place recognition | |
| Kini et al. | 3dmodt: Attention-guided affinities for joint detection & tracking in 3d point clouds | |
| Nguyen et al. | CalibBD: Extrinsic calibration of the LiDAR and camera using a bidirectional neural network | |
| Gong et al. | SkipcrossNets: Adaptive Skip-Cross Fusion for Road Detection: Y. Gong et al. | |
| Xing et al. | ROIFormer: semantic-aware region of interest transformer for efficient self-supervised monocular depth estimation | |
| Ren et al. | Deepsfm: Robust deep iterative refinement for structure from motion | |
| Rohan et al. | A systematic literature review on deep learning-based depth estimation in computer vision | |
| Mohan et al. | Progressive multi-modal fusion for robust 3d object detection | |
| Kim et al. | Adnet: Non-local affinity distillation network for lightweight depth completion with guidance from missing lidar points | |
| Alaba et al. | Multi-sensor fusion 3D object detection for autonomous driving | |
| Hong et al. | Real-time 3D visual perception by cross-dimensional refined learning | |
| Ershadi-Nasab et al. | Uncalibrated multi-view multiple humans association and 3D pose estimation by adversarial learning | |
| Wang et al. | Interactive multi-scale fusion of 2d and 3d features for multi-object tracking | |
| Wang et al. | Person detection, tracking and following using stereo camera | |
| Yu et al. | DMFusion: LiDAR-camera fusion framework with depth merging and temporal aggregation: X. Yu et al. | |
| Jiang et al. | Ffpa-net: Efficient feature fusion with projection awareness for 3d object detection |