DSVP: Dual-Stage Viewpoint Planner for Rapid Exploration by Dynamic Expansion

• Published in: 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) pp. 7623 - 7630
• Main Authors: Zhu, Hongbiao, Cao, Chao, Xia, Yukun, Scherer, Sebastian, Zhang, Ji, Wang, Weidong
• Format: Conference Proceeding
• Language: English
• Published: IEEE 27.09.2021
• Subjects: Benchmark testing; Convolution; Heuristic algorithms; Runtime; Space exploration; Switches; Trajectory
• ISSN: 2153-0866
• DOI: 10.1109/IROS51168.2021.9636473

We present a method for efficiently exploring highly convoluted environments. The method incorporates two planning stages - an exploration stage for extending the boundary of the map, and a relocation stage for explicitly transiting the robot to different sub-areas in the environment. The exploration stage develops a local Rapidly-exploring Random Tree (RRT) in the free space of the environment, and the relocation stage maintains a global graph through the mapped environment, both are dynamically expanded over replanning steps. The method is compared to existing state-of-the-art methods in various challenging simulation and real environments. Experiment comparisons show that our method is twice as efficient in exploring spaces using less processing than the existing methods. Further, we release a benchmark environment to evaluate exploration algorithms as well as facilitate development of autonomous navigation systems. The benchmark environment and our method are open-sourced.