Correlative Scan Matching Position Estimation Method by Fusing Visual and Radar Line Features
-
Published:2023-12-27
Issue:1
Volume:16
Page:114
-
ISSN:2072-4292
-
Container-title:Remote Sensing
-
language:en
-
Short-container-title:Remote Sensing
Author:
Li Yang1, Cui Xiwei1, Wang Yanping1, Sun Jinping2ORCID
Affiliation:
1. Radar Monitoring Technology Laboratory, School of Information Science and Technology, North China University of Technology, Beijing 100144, China 2. School of Electronic and Information Engineering, Beihang University, Beijing 100191, China
Abstract
Millimeter-wave radar and optical cameras are one of the primary sensing combinations for autonomous platforms such as self-driving vehicles and disaster monitoring robots. The millimeter-wave radar odometry can perform self-pose estimation and environmental mapping. However, cumulative errors can arise during extended measurement periods. In particular scenes where loop closure conditions are absent and visual geometric features are discontinuous, existing loop detection methods based on back-end optimization face challenges. To address this issue, this study introduces a correlative scan matching (CSM) pose estimation method that integrates visual and radar line features (VRL-SLAM). By making use of the pose output and the occupied grid map generated by the front end of the millimeter-wave radar’s simultaneous localization and mapping (SLAM), it compensates for accumulated errors by matching discontinuous visual line features and radar line features. Firstly, a pose estimation framework that integrates visual and radar line features was proposed to reduce the accumulated errors generated by the odometer. Secondly, an adaptive Hough transform line detection method (A-Hough) based on the projection of the prior radar grid map was introduced, eliminating interference from non-matching lines, enhancing the accuracy of line feature matching, and establishing a collection of visual line features. Furthermore, a Gaussian mixture model clustering method based on radar cross-section (RCS) was proposed, reducing the impact of radar clutter points online feature matching. Lastly, actual data from two scenes were collected to compare the algorithm proposed in this study with the CSM algorithm and RI-SLAM.. The results demonstrated a reduction in long-term accumulated errors, verifying the effectiveness of the method.
Funder
Beijing Natural Science Foundation National Natural Science Foundation of China Yuyou Talent Training Program of the North China University of Technology
Subject
General Earth and Planetary Sciences
Reference21 articles.
1. Li, Y., Wei, Y., Wang, Y., Lin, Y., Shen, W., and Jiang, W. (2023). False Detections Revising Algorithm for Millimeter Wave Radar SLAM in Tunnel. Remote Sens., 15. 2. Olson, E.B. (2009, January 12–17). Real-time correlative scan matching. Proceedings of the IEEE International Conference on Robotics & Automation, Kobe, Japan. 3. Li, Y., Liu, Y., Wang, Y., Lin, Y., and Shen, W. (2020). The Millimeter-Wave Radar SLAM Assisted by the RCS Feature of the Target and IMU. Sensors, 20. 4. Camera, LiDAR and Multi-modal SLAM Systems for Autonomous Ground Vehicles: A Survey;Chghaf;J. Intell. Robot. Syst.,2022 5. Chen, S., Zhou, B., Jiang, C., Xue, W., and Li, Q. (2021). A LiDAR/Visual SLAM Backend with Loop Closure Detection and Graph Optimization. Remote Sens., 13.
|
|