Affiliation:
1. School of Computer Science, Jiangsu University of Science and Technology, Zhenjiang 212114, China
Abstract
The autonomous navigation of mobile robots contains three parts: map building, global localization, and path planning. Precise pose data directly affect the accuracy of global localization. However, the cumulative error problems of sensors and various estimation strategies cause the pose to have a large gap in data accuracy. To address these problems, this paper proposes a pose calibration method based on localization and point cloud registration, which is called L-PCM. Firstly, the method obtains the odometer and IMU (inertial measurement unit) data through the sensors mounted on the mobile robot and uses the UKF (unscented Kalman filter) algorithm to filter and fuse the odometer data and IMU data to obtain the estimated pose of the mobile robot. Secondly, the AMCL (adaptive Monte Carlo localization) is improved by combining the UKF fusion model of the IMU and odometer to obtain the modified global initial pose of the mobile robot. Finally, PL-ICP (point to line-iterative closest point) point cloud registration is used to calibrate the modified global initial pose to obtain the global pose of the mobile robot. Through simulation experiments, it is verified that the UKF fusion algorithm can reduce the influence of cumulative errors and the improved AMCL algorithm can optimize the pose trajectory. The average value of the position error is about 0.0447 m, and the average value of the angle error is stabilized at about 0.0049 degrees. Meanwhile, it has been verified that the L-PCM is significantly better than the existing AMCL algorithm, with a position error of about 0.01726 m and an average angle error of about 0.00302 degrees, effectively improving the accuracy of the pose.
Funder
National Natural Science Foundation of China
Reference42 articles.
1. An Overview of SLAM;Jia;Proceedings of 2018 Chinese Intelligent Systems Conference,2019
2. Sinisa, M. (2022). Evaluation of SLAM Methods and Adaptive Monte Carlo Localization. [Ph.D. Thesis, Vienna University of Technology].
3. Hanten, R., Buck, S., Otte, S., and Zell, A. (2016, January 3–7). Vector-AMCL: Vector based Adaptive Monte Carlo Localization for Indoor Maps. Proceedings of the 14th International Conference on Intelligent Autonomous Systems (IAS-14), Shanghai, China.
4. An Improved Localization of Mobile Robotic System Based on AMCL Algorithm;Chung;IEEE Sens. J.,2021
5. Garcia, A., Martín, F., Guerrero, J.M., Rodríguez, F.J., and Matellán, V. (June, January 29). Portable multi-hypothesis Monte Carlo localization for mobile robots. Proceedings of the 2023 IEEE International Conference on Robotics and Automation (ICRA), London, UK.