A Method for Indoor Vehicle Obstacle Avoidance by Fusion of Image and LiDAR

Dan Li, Jiaqiang Dong, Kun Zhong, Chenping Zeng and Xun Cao

Background and Objective: In response to the challenges of poor mapping outcomes and susceptibility to obstacles encountered by indoor mobile vehicles relying solely on pure cameras or pure LiDAR during their movements, this paper proposes an obstacle avoidance method for indoor mobile vehicles that integrates image and LiDAR data, thus achieving obstacle avoidance for mobile vehicles. Materials and Methods: This method combines data from a depth camera and LiDAR, employing the Gmapping SLAM algorithm for environmental mapping, along with the A* algorithm and TEB algorithm for local path planning. In addition, this approach incorporates gesture functionality, which can be used to control the vehicle in certain special scenarios where “pseudo-obstacles” exist. The method utilizes the YOLO V3 algorithm for gesture recognition. Results: This paper merges the maps generated by the depth camera and LiDAR, resulting in a three-dimensional map that is more enriched and better aligned with real-world conditions. Combined with the A* algorithm and TEB algorithm, an optimal route is planned, enabling the mobile vehicles to effectively obtain obstacle information and thus achieve obstacle avoidance. Additionally, the introduced gesture recognition feature, which has been validated, also effectively controls the forward and backward movements of the mobile vehicles, facilitating obstacle avoidance. Conclusion: The experimental platform for the mobile vehicles, which integrates depth camera and LiDAR, built in this study has been validated for real-time obstacle avoidance through path planning in indoor environments. The introduced gesture recognition also effectively enables obstacle avoidance for the mobile vehicles.

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