Fast segmentation of 3D point clouds: A paradigm on LiDAR data for autonomous vehicle applications

The recent activity in the area of autonomous vehicle navigation has initiated a series of reactions that stirred the automobile industry, pushing for the fast commercialization of this technology which, until recently, seemed futuristic. The LiDAR sensor is able to provide a detailed understanding of the environment surrounding the vehicle making it useful in a plethora of autonomous driving scenarios. Segmenting the 3D point cloud that is provided by modern LiDAR sensors, is the first important step towards the situational assessment pipeline that aims for the safety of the passengers. This step needs to provide accurate segmentation of the ground surface and the obstacles in the vehicle's path, and to process each point cloud in real time. The proposed pipeline aims to solve the problem of 3D point cloud segmentation for data received from a LiDAR in a fast and low complexity manner that targets real world applications. The two-step algorithm first extracts the ground surface in an iterative fashion using deterministically assigned seed points, and then clusters the remaining non-ground points taking advantage of the structure of the LiDAR point cloud. Our proposed algorithms outperform similar approaches in running time, while producing similar results and support the validity of this pipeline as a segmentation tool for real world applications.