Hostname: page-component-cd9895bd7-jn8rn Total loading time: 0 Render date: 2024-12-23T08:43:32.421Z Has data issue: false hasContentIssue false
  • English
  • Français

Body-fixed SLAM with Local Submaps for Planetary Rover

Published online by Cambridge University Press:  26 June 2019

Bo Zheng ,
Zexu Zhang Open the ORCID record for Zexu Zhang [Opens in a new window] ,
Jing Wang ,
Feng Chen  and
Xiangquan Wei
Bo Zheng
Affiliation:
(School of Astronautics, Harbin Institute of Technology, 150080 Harbin, China)
Zexu Zhang*
Affiliation:
(School of Astronautics, Harbin Institute of Technology, 150080 Harbin, China)
Jing Wang
Affiliation:
(Science and Technology on Optical-Radiation Laboratory, 100000 Beijing, China)
Feng Chen
Affiliation:
(Shanghai Institute of Aerospace System Engineering, 201108, Shanghai, China)
Xiangquan Wei
Affiliation:
(Shanghai Institute of Aerospace System Engineering, 201108, Shanghai, China)
*
(E-mail: [email protected])
Get access Rights & Permissions [Opens in a new window]

Abstract

In traditional Simultaneous Localisation and Mapping (SLAM) algorithms based on Extended Kalman Filtering (EKF-SLAM), the uncertainty of state estimation will increase rapidly with the development of the exploration process and the increase of map area. Likewise, the computational complexity of the EKF-SLAM is proportional to the square of the number of feature points contained in the state variables in a single filtering process. A new SLAM algorithm combining the local submaps and the body-fixed coordinates of the rover is presented in this paper. The algorithm can reduce the computational complexity and enhance computational speed in consideration of the processing capability of the onboard computer. Due to the introduction of local submaps, the algorithm represented in this paper is able to reduce the number of feature points contained in the state variables in each single filtering process. Therefore, the algorithm could reduce the computational complexity and improve the computational speed. In addition, rover body-fixed SLAM could improve the navigation accuracy of a rover and decrease the cumulative linearization error by coordinates transformation during the update process, which is shown in the simulation results.

Keywords

Planetary Surface ExplorationEKF-SLAMLocal SubmapsBody-fixed SLAM
Type
Research Article
Information
The Journal of Navigation , Volume 73 , Issue 1 , January 2020 , pp. 149 - 171
Copyright
Copyright © The Royal Institute of Navigation 2019 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Dissanayake, M.G., Newman, P., Clark, S., Durrant-Whyte, H.F. and Csorba, M. (2001). A solution to the simultaneous localization and map building (SLAM) problem. IEEE Transactions on robotics and automation. 17(3), 229–241.Google Scholar
Islam, M.R., Chowdhury, F.H., Rezwan, S., Ishaque, M.J., Akanda, J.U., Tuhel, A.S. and Riddhe, B.B. (2017). Novel design and performance analysis of a Mars exploration robot: Mars rover mongol pothik. In Research in Computational Intelligence and Communication Networks (ICRCICN). 132–136. Kolkata, India.Google Scholar
Joly, C. and Rives, P. (2009). Building consistent local submaps with omnidirectional SLAM. Computer Vision Workshops, 2009 IEEE 12th International Conference on IEEE. 2180–2187. Kyoto, Japan.Google Scholar
Leonard, J.J. and Durrant-Whyte, H.F. (1991). Mobile robot localization by tracking geometric beacons. IEEE Transactions on robotics and Automation. 7(3), 376–382.Google Scholar
Martinez-Cantin, R. and Castellanos, J.A. (2006). Bounding uncertainty in EKF-SLAM: The robocentric local approach. In Robotics and Automation, 2006. ICRA 2006. Proceedings 2006 IEEE International Conference on IEEE. 430–435. Orlando, FL, USA.Google Scholar
Meng, Y. and Hutao, C. (2014). A new approach based on crater detection and matching for visual navigation in planetary landing. Advances in Space Research. 53(12), 1810–1821.Google Scholar
Martinez-Cantin, R. and Castellanos, J.A. (2005). Unscented SLAM for large-scale outdoor environments. Intelligent Robots and Systems. 2005 IEEE/RSJ International Conference on. IEEE. Edmonton, Alta, Canada.Google Scholar
Nüchter, A., Lingemann, K., Hertzberg, J. and Surmann, H. (2007). 6D SLAM—3D mapping outdoor environments. Journal of Field Robotics. 24(8–9), 699–722.Google Scholar
Paz, L.M. and Neira, J. (2006). Optimal local map size for EKF-based SLAM. In Intelligent Robots and Systems, 2006 IEEE/RSJ International Conference on IEEE. 5019–5025. Beijing, China.Google Scholar
Tan, W., Liu, H., Dong, Z., Zhang, G. and Bao, H. (2013). Robust monocular SLAM in dynamic environments. In Mixed and Augmented Reality (ISMAR), 2013 IEEE International Symposium. 209–218. Adelaide, SA, Australia.Google Scholar
Thrun, S., Montemerlo, M., Koller, D., Wegbreit, B., Nieto, J. and Nebot, E. (2004). Fastslam: An efficient solution to the simultaneous localization and mapping problem with unknown data association. Journal of Machine Learning Research, 4(3), 380–407.Google Scholar
Wang, J., Song, J., Zhao, L. and Huang, S. (2018). A Submap Joining Based RGB-D SLAM Algorithm Using Planes as Features. In Field and Service Robotics. 367–382. ETH Zurich, Switzerland.Google Scholar
Yen, J., Jain, A. and Balaram, J. (1999). ROAMS: Rover Analysis Modeling and Simulation Software. In Artificial Intelligence, Robotics and Automation in Space, 440, 249. Noordwijk, Netherland.Google Scholar
Zhuo, Z., Yang, S., Zexu, Z. and Weisheng, Y. (2018a). New Results on Sliding-Mode Control for Takagi-Sugeno Fuzzy Multiagent Systems. IEEE Transactions on Cybernetics. (99), 1–13.Google Scholar
Zhuo, Z., Zexu, Z. and Hui, Z. (2018b). Distributed Attitude Control for Multi-Spacecraft via Takagi-Sugeno Fuzzy Approach. IEEE Transactions on Aerospace and Electronic Systems. 54(2), 642–654.Google Scholar
Zhuo, Z., Yang, S., Zexu, Z., Hui, Z. and Sheng, B. (2018c). Modified Order-Reduction Method for Distributed Control of Multi-Spacecraft Networks With Time-Varying Delays. IEEE Transactions on Control of Network Systems, 5(1), 79–92.Google Scholar