A Lightweight Localization Strategy for LiDAR-Guided Autonomous Robots with Artificial Landmarks
| Autor: | Qi Song, Sen Wang, Qinglei Zhao, Yongyao Li, Guanyu Ding, Yan Gong, Xiao-He Chen, Wen-Chang Xu |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
0209 industrial biotechnology
Matching (graph theory) Computer science Movement Reflector (antenna) 02 engineering and technology TP1-1185 01 natural sciences Biochemistry Odometer Article Analytical Chemistry Compensation (engineering) Motion LiDAR navigation 020901 industrial engineering & automation Robustness (computer science) Computer vision Electrical and Electronic Engineering Instrumentation Motion compensation business.industry high-speed movement Chemical technology 010401 analytical chemistry reflector matching Robotics Atomic and Molecular Physics and Optics 0104 chemical sciences motion compensation Lidar reflector localization Robot Artificial intelligence business Algorithms |
| Zdroj: | Sensors Volume 21 Issue 13 Sensors, Vol 21, Iss 4479, p 4479 (2021) Sensors (Basel, Switzerland) |
| ISSN: | 1424-8220 |
| DOI: | 10.3390/s21134479 |
| Popis: | This paper proposes and implements a lightweight, “real-time” localization system (SORLA) with artificial landmarks (reflectors), which only uses LiDAR data for the laser odometer compensation in the case of high-speed or sharp-turning. Theoretically, due to the feature-matching mechanism of the LiDAR, locations of multiple reflectors and the reflector layout are not limited by geometrical relation. A series of algorithms is implemented to find and track the features of the environment, such as the reflector localization method, the motion compensation technique, and the reflector matching optimization algorithm. The reflector extraction algorithm is used to identify the reflector candidates and estimates the precise center locations of the reflectors from 2D LiDAR data. The motion compensation algorithm predicts the potential velocity, location, and angle of the robot without odometer errors. Finally, the matching optimization algorithm searches the reflector combinations for the best matching score, which ensures that the correct reflector combination could be found during the high-speed movement and fast turning. All those mechanisms guarantee the algorithm’s precision and robustness in the high speed and noisy background. Our experimental results show that the SORLA algorithm has an average localization error of 6.45 mm at a speed of 0.4 m/s, and 9.87 mm at 4.2 m/s, and still works well with the angular velocity of 1.4 rad/s at a sharp turn. The recovery mechanism in the algorithm could handle the failure cases of reflector occlusion, and the long-term stability test of 72 h firmly proves the algorithm’s robustness. This work shows that the strategy used in the SORLA algorithm is feasible for industry-level navigation with high precision and a promising alternative solution for SLAM. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
| Nepřihlášeným uživatelům se plný text nezobrazuje | K zobrazení výsledku je třeba se přihlásit. |