| Citation: | ZHANG S F，LI Y Y，ZHANG T. Adaptive Monte Carlo localization algorithm based on fast affine template matching[J]. Journal of Beijing University of Aeronautics and Astronautics，2023，49（11）：2898-2905 （in Chinese） doi: 10.13700/j.bh.1001-5965.2022.0001
|
Autonomous positioning is an important task of mobile robots, and the problem of robot kidnapping is a difficult point in positioning technology. The adaptive Monte Carlo localization (AMCL) algorithm based on particle filtering can solve the problem of robot kidnapping, but it needed to put new particles on the global map during the positioning recovery process, resulting in low recovery efficiency. An adaptive Monte Carlo localization technique based on fast affine template matching (AMCL-FM) is proposed through research on the adaptive Monte Carlo localization algorithm and the idea of template matching in image science. The algorithm uses the global cost map and the local cost map to estimate the true position of the robot and then places new particles at the estimated position, which improves the effectiveness of the new particles. This algorithm’s positioning accuracy and positioning recovery effectiveness are both up 61.13% and 69.23% from adaptive Monte Carlo localization algorithm, respectively.
| [1] |
刘明阳, 郑晨, 张昂, 等. 一种轮式机器人轨迹跟踪控制算法的研究[J]. 现代导航, 2020, 11(4): 268-271. doi: 10.3969/j.issn.1674-7976.2020.04.007
LIU M Y, ZHENG C, ZHANG A, et al. Research on a wheeled robot tracking control algorithm[J]. Modern Navigation, 2020, 11(4): 268-271(in Chinese). doi: 10.3969/j.issn.1674-7976.2020.04.007
|
| [2] |
张立志, 陈殿生, 刘维惠. 基于混合地图的护理机器人室内导航方法[J]. 北京航空航天大学学报, 2018, 44(5): 991-1000. doi: 10.13700/j.bh.1001-5965.2017.0325
ZHANG L Z, CHEN D S, LIU W H. Care robot indoor navigation method based on hybrid map[J]. Journal of Beijing University of Aeronautics and Astronautics, 2018, 44(5): 991-1000(in Chinese). doi: 10.13700/j.bh.1001-5965.2017.0325
|
| [3] |
BUKHORI I, ISMAIL Z H, NAMERIKAWA T. Detection strategy for kidnapped robot problem in landmark-based map Monte Carlo localization[C]//Proceedings of the IEEE International Symposium on Robotics and Intelligent Sensors. Langkawi: IRIS, 2015: 75-80.
|
| [4] |
FOX D, BURGARD W, KRUPPA H, et al. A probabilistic approach to collaborative multi-robot localization[J]. Autonomous Robots, 2000, 8(3): 325-344. doi: 10.1023/A:1008937911390
|
| [5] |
BLOK P M, BOHEEMEN K V, EVERT F V, et al. Robot navigation in orchards with localization based on particle filter and Kalman filter[J]. Computers and Electronics in Agriculture, 2019, 157: 261-269. doi: 10.1016/j.compag.2018.12.046
|
| [6] |
BACCA B, SALVI J, CUFÍ X. Long-term mapping and localization using feature stability histograms[J]. Robotics and Autonomous Systems, 2013, 61(12): 1539-1558. doi: 10.1016/j.robot.2013.07.003
|
| [7] |
KARKUS P, HSU D, LEE W S. Particle filter networks: End-to-end probabilistic localization from visual observations[EB/OL]. (2018-10-25)[2022-01-01]. https://arxiv.org/abs/1805.08975v1.
|
| [8] |
ZHANG L, ZAPATA R, LEPIBAY P. Self-adaptive Monte Carlo for single-robot and multi-robot localization[C]//Proceedings of the IEEE International Conference on Automation and Logistics. Piscataway: IEEE Press, 2009: 1927-1933.
|
| [9] |
CHEN R, YIN H, JIAO Y, et al. Deep samplable observation model for global localization and kidnapping[J]. IEEE Robotics and Automation Letters, 2021, 6(2): 2296-2303. doi: 10.1109/LRA.2021.3061339
|
| [10] |
AKAI N, HIRAYAMA T, MURASE H. Hybrid localization using model-and learning-based methods: Fusion of Monte Carlo and E2E localizations via importance sampling[C]//Proceedings of the IEEE International Conference on Robotics and Automation. Piscataway: IEEE Press, 2020: 6469-6475.
|
| [11] |
CHUNG M A, LIN C W. An improved localization of mobile robotic system based on AMCL algorithm[J]. IEEE Sensors Journal, 2021, 22(1): 900-908.
|
| [12] |
冯佳萌, 裴东, 邹勇, 等. 基于机器人激光定位的一种改进AMCL算法[J]. 激光与光电子学进展, 2021, 58(20): 2028003.
FENG J M, PEI D, ZOU Y, et al. An improved AMCL algorithm based on robot laser localization[J]. Laser & Optoelectronics Progress, 2021, 58(20): 2028003(in Chinese).
|
| [13] |
GE G, ZHANG Y, WANG W, et al. Text-MCL: Autonomous mobile robot localization in similar environment using text-level semantic information[J]. Machines, 2022, 10(3): 169. doi: 10.3390/machines10030169
|
| [14] |
JIA D, CAO J, SONG W, et al. Colour FAST (CFAST) match: Fast affine template matching for colour images[J]. Electronics Letters, 2016, 52(14): 1220-1221. doi: 10.1049/el.2016.1331
|
| [15] |
THRUN S, BURGARD W, FOX D. 概率机器人[M]. 曹红玉, 谭志, 史晓霞, 等译. 北京: 机械工业出版社, 2017: 187-203.
THRUN S, BURGARD W, FOX D. Probabilistic robotics[M]. CAO H Y, TAN Z, SHI X X, et al, translated. Beijing: Machinery Industry Press, 2017: 187-203(in Chinese).
|
| [16] |
GRISETTI G, STACHNISS C, BURGARD W. Improved techniques for grid mapping with Rao-Blackwellized particle filters[J]. IEEE Transactions on Robotics, 2007, 23(1): 34-46. doi: 10.1109/TRO.2006.889486
|
Figures(14) / Tables(2)
Copyright © Journal of Beijing University of Aeronautics and Astronautics
Address: Editorial Department of Journal of Beijing University of Aeronautics and Astronautics, 37 Xueyuan Road, Haidian District, Beijing Post Code: 100191 Email: jbuaa@buaa.edu.cn
Supported by:
Beijing Renhe Information Technology Co., Ltd.
DownLoad:
