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Open AccessArticle

MIM_SLAM: A Multi-Level ICP Matching Method for Mobile Robot in Large-Scale and Sparse Scenes

by Jingchuan Wang 1,2,*, Ming Zhao 1,2 and Weidong Chen 1,2
1
Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China
2
Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2018, 8(12), 2432; https://doi.org/10.3390/app8122432
Received: 19 October 2018 / Revised: 24 November 2018 / Accepted: 27 November 2018 / Published: 30 November 2018
(This article belongs to the Special Issue Advanced Mobile Robotics)
In large-scale and sparse scenes, such as farmland, orchards, mines, and substations, 3D simultaneous localization and mapping are challenging matters that need to address issues such as maintaining reliable data association for scarce environmental information and reducing the computational complexity of global optimization for large-scale scenes. To solve these problems, a real-time incremental simultaneous localization and mapping algorithm called MIM_SLAM is proposed in this paper. This algorithm is applied in mobile robots to build a map on a non-flat road with a 3D LiDAR sensor. MIM_SLAM’s main contribution is that multi-level ICP (Iterative Closest Point) matching is used to solve the data association problem, a Fisher information matrix is used to describe the uncertainty of the estimated pose, and these poses are optimized by the incremental optimization method, which can greatly reduce the computational cost. Then, a map with a high consistency will be established. The proposed algorithm has been evaluated in the real indoor and outdoor scenes as well as two substations and benchmarking dataset from KITTI with the characteristics of sparse and large-scale. Results show that the proposed algorithm has a high mapping accuracy and meets the real-time requirements. View Full-Text
Keywords: data association; 3D-SLAM; localization; mapping data association; 3D-SLAM; localization; mapping
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MDPI and ACS Style

Wang, J.; Zhao, M.; Chen, W. MIM_SLAM: A Multi-Level ICP Matching Method for Mobile Robot in Large-Scale and Sparse Scenes. Appl. Sci. 2018, 8, 2432.

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