Real-Time Segmentation of Sparse 3D Point Clouds on a TX2

Abstract

In this paper, we propose a real-time 3D object detection method, which from sparse 3D point clouds and based on NVIDIA Jetson TX2. This effective method first uses the external parameter matrix to transform the coordinates of the point clouds data, then supplements the missing data in the original data through the interpolation algorithm, and then uses the RANSAC algorithm to remove the ground, uses the improved DBSCAN clustering algorithm, which clustering segmentation is performed for the point clouds fusion partition distance threshold and angle threshold of different distance intervals. Finally, we regress the obstacle boundary to the mini-box by gradient descent, output for the result is visualized in the form of cuboid detection. Compared to traditional DBSCAN algorithm, our method reduces the missed detection rate and improves the real-time performance. Above all, we implements our approach in C++ and ROS, and the experimental results show that our method produces high quality object detection by using NVIDIA Jetson TX2 online.