[1]LUO Yuan,YU Jiahang,WANG Longfeng,et al.Simultaneous localization and mapping of an improved RBPF based mobile robot[J].CAAI Transactions on Intelligent Systems,2015,10(3):460-464.[doi:10.3969/j.issn.1673-4785.201404024]
Copy
Simultaneous localization and mapping of an improved RBPF based mobile robot
CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
10
Number of periods:
2015 3
Page number:
460-464
Column:
学术论文—智能系统
Public date:
2015-06-25
- Title:
- Simultaneous localization and mapping of an improved RBPF based mobile robot
- Keywords:
- mobile robot; Rao-Blackwellized particle filter; simultaneous localization and mapping (SLAM); Gmapping algorithm; adaptive resampling methods
- CLC:
- TP242.6
- DOI:
- 10.3969/j.issn.1673-4785.201404024
- Abstract:
- As in the research of simultaneous localization and mapping (SLAM) of mobile robot applying traditional Rao-Blackwellized particle filter, the computational complexity is too high and memory space usage is too large, which causes poor real-time performance, an improved approach is proposed. Among a group of particles gathering in a particular state, the statistical properties of particles are identical. By applying the Kalman updating step to one representative particle in the group of particles, and using it repeatedly in the same group, the complexity is reduced and arithmetic speed is improved. Combining the proposed distribution and adaptive resampling methods from the Gmapping algorithm, the results of actual experiment carried out with Pioneer III robot and ROS platform illustrate that the real-time performance of the proposal could be enhanced while ensuring the quality of grid map.
- References:
-
[1] SMITH R, SELF M, CHEESEMAN P. Estimating uncertain spatial relationships in robotics[M]//COX I J, WILFONG G T. Autonomous Robot Vehicles. New York: Springer, 1990: 167-193.
[2] DISSANAYAKE G, HUANG S, WANG Z, et al. A review of recent developments in simultaneous localization and mapping[C]//6th International Conference on Industrial and Information Systems (ICIIS). Kandy, Sri Lanka, 2011: 477-482.
[3] SMITH R C, CHEESEMAN P. On the representation and estimation of spatial uncertainty[J]. The International Journal of Robotics Research, 1986, 5(4): 56-68.
[4] 张文玲, 朱明清, 陈宗海. 基于强跟踪UKF的自适应 SLAM 算法[J]. 机器人, 2010, 32(2): 190-195. ZHANG Wenling, ZHU Mingqing, CHEN Zonghai. An adaptive SLAM algorithm based on strong tracking UKF[J]. Robot, 2010, 32(2): 190-195.
[5] DOUCET A, De FREITAS N, MURPHY K, et al. Rao-Blackwellised particle filtering for dynamic Bayesian networks[C]//Proceedings of the Sixteenth Conference on Uncertainty in Artificial Intelligence. San Francisco, USA, 2000: 176-183.
[6] QU Liping, WANG Hongjian. An overview of robot SLAM problem[C]//International Conference on Consumer Electronics, Communications and Networks (CECNet). Xianning, China, 2011: 1953-1956.
[7] GRISETTI G, STACHNISS C, BURGARD W. Improved techniques for grid mapping with Rao-Blackwellized particle filters[J]. Robotics, 2007, 23(1): 34-46.
[8] 张建伟, 张立新, 胡颖, 等. 开源机器人操作系统—ROS[M]. 北京: 科学出版社, 2012: 1-6.
[9] GERKEY B. Gmapping.[EB/OL]. [2010-08-05]. http://wiki.ros.org/slam_gmapping.
[10] GRISETTI G, STACHNISS C, BURGARD W. Improving grid-based slam with Rao-Blackwellized particle filters by adaptive proposals and selective resampling[C]//Proceedings of the 2005 IEEE International Conference on Robotics and Automation. Barcelona, Spain, 2005: 2432-2437.
[11] DOUCET A, De FREITAS N, GORDON N. An introduction to sequential Monte Carlo methods[M]//DOUCET A, DE FREITAS N, GORDON N. Sequential Monte Carlo Methods in Practice. New York: Springer, 2001: 3-14.
[12] De FREITAS N. Rao-Blackwellised particle filtering for fault diagnosis[C]//2002 IEEE Aerospace Conference Proceedings. Big Sky, USA, 2002, 4: 1767-1772.
- Similar References:
Memo
-
Last Update:
2015-07-15