Research on Lidar Point Cloud Tracking Method Based on Kalman Filtering
Due to the limited detection range,and the vulnerability tolight occlusion and scattering,the integrity and cor-rectness of target point cloud features can not always be guaranteedby lidar in the tracking process.Hence,target fails to be tracked sometimes.In order to improve the robustness ofthe tracking process,a laser point cloud tracking method based on Kalman filtering is proposed.Firstly,the bounding box set is established for the clustered point cloud,and then the Kalman filter model is used to develop the tracking framefor the bounding box.The optimal estimate of the tracking framein the previous frame and the bounding box in the current frame are matched by means of the intersection over union(IoU)method.Then,the maximum correlation result is taken as the target point cloud of the current frame,and the Kalman filtering model parameters are also updated at the same time.The target point cloud can be tracked in the real-time way.
environment perceptiontarget trackingpoint cloud processingKalman filteringintersection over union
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