IJAMCE
International Journal of Applied Mathematics in Control Engineering
Published by Qingdao University of Technology  ·  Est. 2018
Open Access Peer Reviewed
● Open Access

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With advancements in robotics, mobile robots are increasingly deployed in diverse applications. However, complex indoor environments pose challenges for perception and obstacle recognition. This paper proposes a costmap-based multi-sensor fusion method that integrates visual point clouds with two 2D LiDARs for obstacle detection. A downward-facing LiDAR enables ground obstacle detection, with systematic functional design and parameter optimization. Experimental results demonstrate effective 3D obstacle perception and improved obstacle avoidance, achieving an average response time of 1.09 s for static obstacles and 1.30 s for dynamic obstacles.

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Abstract

With advancements in robotics, mobile robots are increasingly deployed in diverse applications. However, complex indoor environments pose challenges for perception and obstacle recognition. This paper proposes a costmap-based multi-sensor fusion method that integrates visual point clouds with two 2D LiDARs for obstacle detection. A downward-facing LiDAR enables ground obstacle detection, with systematic functional design and parameter optimization. Experimental results demonstrate effective 3D obstacle perception and improved obstacle avoidance, achieving an average response time of 1.09 s for static obstacles and 1.30 s for dynamic obstacles.

Keywords:Multi-SensorCostmapObstacle RecognitionObstacle Avoidance 1 |

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