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Navigation of a mobile robot in a dynamic environment using a point cloud map

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Abstract

In this paper, we consider autonomous navigation of a wheeled mobile robot in a dynamic environment using a 3D point cloud map. We consider four kinds of 2D maps: static global map, dynamic global map, global cost map, and local cost map; to plan a feasible path of the robot to adapt to a dynamic environment. We consider a mobile robot for plant patrolling in a 3D environment with plane slopes but not rough terrain for which a 2D environment map suffices. We propose 2D static global map for robot navigation by projecting prior measured 3D point cloud map data on a horizontal plane with considering the climbing ability of the robot. We also build a 2D dynamic global map by projecting a real-time 3D point cloud on the 2D static global map by SLAM. Accumulated errors of SLAM can be canceled using some landmarks placed in the environment. A global planner calculates an optimal global path that minimizes the distance from an initial robot pose (position and orientation) to a goal pose (position and orientation) by A* algorithm based on the global cost map which is built from the dynamic global map. However, this process should take much time. To avoid moving obstacles, the TEB (Timed Elastic Band) local planner is used to calculate an optimal local path based on a local cost map which is given by a real-time local 3D point cloud. To demonstrate the effectiveness of the proposed system, experiments were carried out. In the experiment, we use an AR card as a landmark for simplification of implementation. We prove that the robot can navigate in a dynamic environment and accumulated errors can be canceled by the AR cards placed in the environment as landmarks.

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Notes

  1. The method [19] can be also applicable to project the points into a local ground. It can segment multiple planes. However, it requires a higher density 3D point cloud and takes much time for processing. In this phase, we consider a local area and can assume that there is only one ground plane around the robot roughly. We can apply faster algorithm to segment one plane such as [20, 21].

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Acknowledgements

A part of this study was carried out as a collaborative research with BESTERRA Co., Ltd.

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Authors and Affiliations

  1. Kyoto University, Nishikyo, Kyoto, Japan

    Xixun Wang, Makoto Tada & Fumitoshi Matsuno

  2. Yamaguchi University, Yamaguchi, Japan

    Yoshiki Mizukami

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  1. Xixun Wang
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  2. Yoshiki Mizukami
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  3. Makoto Tada
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  4. Fumitoshi Matsuno
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Correspondence to Xixun Wang.

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Wang, X., Mizukami, Y., Tada, M. et al. Navigation of a mobile robot in a dynamic environment using a point cloud map. Artif Life Robotics 26, 10–20 (2021). https://doi.org/10.1007/s10015-020-00617-3

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  • DOI: https://doi.org/10.1007/s10015-020-00617-3

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