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Enhanced Autonomous Exploration and Mapping of an Unknown Environment with the Fusion of Dual RGB-D Sensors
Ningbo Yu, Shirong Wang
Engineering ›› 2019, Vol. 5 ›› Issue (1) : 164-172.
PDF(1853 KB)
PDF(1853 KB)
Enhanced Autonomous Exploration and Mapping of an Unknown Environment with the Fusion of Dual RGB-D Sensors
The autonomous exploration and mapping of an unknown environment is useful in a wide range of applications and thus holds great significance. Existing methods mostly use range sensors to generate two-dimensional (2D) grid maps. Red/green/blue-depth (RGB-D) sensors provide both color and depth information on the environment, thereby enabling the generation of a three-dimensional (3D) point cloud map that is intuitive for human perception. In this paper, we present a systematic approach with dual RGB-D sensors to achieve the autonomous exploration and mapping of an unknown indoor environment. With the synchronized and processed RGB-D data, location points were generated and a 3D point cloud map and 2D grid map were incrementally built. Next, the exploration was modeled as a partially observable Markov decision process. Partial map simulation and global frontier search methods were combined for autonomous exploration, and dynamic action constraints were utilized in motion control. In this way, the local optimum can be avoided and the exploration efficacy can be ensured. Experiments with single connected and multi-branched regions demonstrated the high robustness, efficiency, and superiority of the developed system and methods.
Autonomous exploration / Red/green/blue-depth / Sensor fusion / Point cloud / Partial map simulation / Global frontier search
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The authors would like to thank Mathieu Labbé from Université de Sherbrooke, Canada, and Mikko Lauri from Tampere University of Technology, Finland, for their helpful suggestions.
The authors would like to acknowledge the National Natural Science Foundation of China (61720106012 and 61403215), the Foundation of State Key Laboratory of Robotics (2006-003) and the Fundamental Research Funds for the Central Universities for the financial support of this work.
Ningbo Yu and Shirong Wang declare that they have no conflict of interest or financial conflicts to disclose.
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