School of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun 130022, China.
Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China.
Sensors (Basel). 2021 Jun 30;21(13):4479. doi: 10.3390/s21134479.
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.
本文提出并实现了一种使用人工地标（反光器）的轻量级、“实时”定位系统（SORLA），该系统仅在高速或急转弯时使用激光雷达数据进行激光测距仪补偿。理论上，由于激光雷达的特征匹配机制，多个反光器的位置和反光器布局不受几何关系的限制。实现了一系列算法来寻找和跟踪环境特征，例如反光器定位方法、运动补偿技术和反光器匹配优化算法。反光器提取算法用于识别反光器候选对象，并从 2D 激光雷达数据中估计反光器的精确中心位置。运动补偿算法预测机器人在没有里程计误差的情况下的潜在速度、位置和角度。最后，匹配优化算法搜索反光器组合以获得最佳匹配分数，这确保了在高速运动和快速转弯时可以找到正确的反光器组合。所有这些机制都保证了算法在高速和嘈杂背景下的精度和鲁棒性。我们的实验结果表明，SORLA 算法在 0.4m/s 的速度下的平均定位误差为 6.45mm，在 4.2m/s 的速度下的平均定位误差为 9.87mm，在 1.4rad/s 的急转弯角速度下仍能正常工作。算法中的恢复机制可以处理反光器遮挡的故障情况，72 小时的长期稳定性测试有力地证明了算法的鲁棒性。这项工作表明，SORLA 算法中使用的策略对于高精度的工业级导航是可行的，是 SLAM 的一种有前途的替代解决方案。
