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Multisensor Data Fusion for Reliable Obstacle Avoidance

Thanh Nguyen CanhTruong Son NguyenCong Hoang QuachXiem HoangVanManh Duong Phung
Dec 2022
摘要
In this work, we propose a new approach that combines data from multiplesensors for reliable obstacle avoidance. The sensors include two depth camerasand a LiDAR arranged so that they can capture the whole 3D area in front of therobot and a 2D slide around it. To fuse the data from these sensors, we firstuse an external camera as a reference to combine data from two depth cameras. Aprojection technique is then introduced to convert the 3D point cloud data ofthe cameras to its 2D correspondence. An obstacle avoidance algorithm is thendeveloped based on the dynamic window approach. A number of experiments havebeen conducted to evaluate our proposed approach. The results show that therobot can effectively avoid static and dynamic obstacles of different shapesand sizes in different environments.
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