Moving object detection under dynamic background in 3D range data

We proposed an unsupervised algorithm to extract profile features and detect moving object under dynamic background in 3D range Data. Moving object detection under dynamic background has become an increasingly popular research topic in mobile robotics. For the characteristics of dynamic background scene, we proposed an online unsupervised moving object detection algorithm, based on Gaussian Mixture Models and Motion Compensation. Furthermore, we did the work of clustering and identifying of the targets. In order to improve the robustness of the algorithm, we used a tracker to track the results of the detection. At last, experimental results on real laser data depicting urban and rural scenes under static and dynamic background are presented.