A combined clustering and image mapping based point cloud segmentation for 3D object detection

3D Object Detection is important to avoid collision and path planning in field of autonomous vehicle. In this paper, we present a combined clustering and image mapping-based algorithm to segment 3D point cloud. It not only provides a dependable initial value as the seeds to cluster the class of objects, but also avoid the pre-trained classifier to detect the objects. We get an accurate 3D object detection result using our proposed algorithm. The proposed algorithm can reduce the computation complexity at the step of determining bounding area in 2D image and produce the initial center of cluster of each object at the step of segmentation in 3D point cloud. The experiment states that the proposed algorithm can improve the accuracy and feasibility of object detection.

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