A localization method of wall-climbing robot based on lidar and improved AMCL

被引：0
|
作者：
Wang Z. [1 ]
Yan B. [1 ]
Dong M. [1 ]
Wang J. [1 ]
Sun P. [1 ]
机构：
[1] Key Laboratory of the Ministry of the Education for Optoelecronic Measurement Technology and Instrument, Beijing Information Science & Technology University, Beijing
来源：
Yi Qi Yi Biao Xue Bao/Chinese Journal of Scientific Instrument | 2022年 / 43卷 / 12期
关键词：
adaptive Monte Carlo; genetic algorithm; particles depletion; PL-ICP; pose tracking;
D O I：
10.19650/j.cnki.cjsi.J2210261
中图分类号：
学科分类号：
摘要：
In practical applications, the boiler wall-climbing robot has some problems, such as limited use of wheel encoders and inertial measurement units (IMU), and easy slippage between the wheels and the metal water wall. In the view of the above factors leading to the decline of robot global localization and pose tracking performance, a robot global localization method based on the laser odometry and the improved adaptive Monte Carlo localization (AMCL) is proposed. Firstly, the laser odometry based on PL-ICP method is adopted to replace the traditional wheel and inertia odometry. Secondly, the idea of DNA cross mutation in genetics is introduced into the particle sampling process of AMCL algorithm to design an improved AMCL method based on genetic algorithm, which is able to alleviate the problems of posture tracking performance degradation and slow location recovery caused by AMCL particle depletion. Experimental results show that the absolute localization error of this method is controlled within 12. 7 cm and the accuracy is 32. 4% higher than that of AMCL method. The localization result of this method is almost unaffected when the robot slips slightly. The speed of the system to restore the localization is 35% higher than that of the ordinary AMCL method when the robot slips greatly. © 2022 Science Press. All rights reserved.
引用
收藏
页码：220 / 227
页数：7
相关论文
共 22 条
  • [1] ZHANG X J, LIU CH H, YU ZH Q., Research and development of water wall wall wear testing robot, Machine Design & Research, 34, 1, pp. 1-4, (2018)
  • [2] LU X Q, LIU G, ZHENG X X, Et al., High temperature corrosion mechanism and robot detection technology of the water wall tube, Journal of Shanghai Jiaotong University, pp. 93-96, (2008)
  • [3] CHEN Z H, PEI H Y, WANG J K, Et al., Survey of monocular camera-based visual relocalization, Robot, 43, 3, pp. 373-384, (2021)
  • [4] DELLAERT F, FOX D, BURGARD W, Et al., Monte Carlo localization for mobile robots, Proceedings of the 1999 IEEE International Conference on Robotics and Automation, pp. 1322-1328, (1999)
  • [5] YANG AO L, JIN H Z, CHEN L, Et al., Mobile robot relocalization method fusing deep learning and particle filtering, Chinese Journal of Scientific Instrument, 42, 7, pp. 226-233, (2021)
  • [6] KUPTAMETEE C, AUNSRI N., A review of resampling techniques in particle filtering framework, Measurement, (2022)
  • [7] JIANG L, NIE W K, ZHU J Y, Et al., Improved AMCL relocation algorithm based on semantic map with corner information, Journal of Mechanical Engineering, pp. 1-12
  • [8] YING W, SUN S., An improved Monte Carlo localization using optimized iterative closest point for mobile robots, Cognitive Computation and Systems, 4, 1, pp. 20-30, (2022)
  • [9] ZHANG L, ZAPATA R, LEPINAY P., Self-adaptive Monte Carlo localization for mobile robots using range finders, Robotica, 30, 2, pp. 229-244, (2012)
  • [10] ZHANG SH F, LI Y Y, ZHANG T., Adaptive Monte Carlo localization algorithm based on fast affine template matching, Journal of Beijing University of Aeronautics and Astronautics, pp. 1-10
123