In this paper, we propose a novel and efficient grid-based Rao-Blackwellized particle filter simultaneous localization and mapping (RBPF-SLAM) with interparticle map shaping (IPMS). The proposed method aims at saving the computational memory in the grid-based RBPF-SLAM while maintaining the mapping accuracy. Unlike conventional RBPF-SLAM in which each particle has its own map of the whole environment, each particle has only a small map of the nearby environment called an individual map in the proposed method. Instead, the map of the remaining large environment is shared by the particles. The part shared by the particles is called a base map. If the individual small maps become reliable enough to trust, they are merged with the base map. To determine when and which part of an individual map should be merged with the base map, we propose two map sharing criteria. Finally, the proposed IPMS RBPF-SLAM is applied to the real-world datasets and benchmark datasets. The experimental results show that our method outperforms conventional methods in terms of map accuracy versus memory consumption.
Bibliographical noteFunding Information:
Manuscript received July 27, 2016; revised January 8, 2017, July 17, 2017, and November 1, 2017; accepted January 3, 2018. Date of publication January 18, 2018; date of current version April 16, 2018. Recommended by Technical Editor K.-S. Hwang. This work was supported by the Industrial Convergence Core Technology Development Program (Development of Robot Intelligence Technology for Mobility with Learning Capability Toward Robust and Seamless Indoor and Outdoor Autonomous Navigation) funded by the Ministry of Trade, Industry, and Energy, South Korea, under Grant 10063172. (Corresponding author: Euntai Kim.) The authors are with the Department of Electrical and Electronic Engineering, Yonsei University, Seoul 120-749, South Korea (e-mail: hygijo@ yonsei.ac.kr; firstname.lastname@example.org; email@example.com; firstname.lastname@example.org).
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All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Computer Science Applications
- Electrical and Electronic Engineering