3D Lidar-based static and moving obstacle detection in driving environments: An approach based on voxels and multi-region ground planes

•A complete framework for ground surface estimation and static/moving obstacle detection in driving environments is proposed.•A piecewise surface fitting algorithm, based on a ‘multi-region’ strategy and Velodyne LIDAR scans behavior is proposed to estimate a finite set of multiple surfaces that fit the road and its vicinity.•A 3D voxel-based representation, using discriminative analysis is proposed for obstacle modeling. The proposed approach detects moving obstacles by integrating and processing information from previous measurements.•A set of diversified experiments, and corresponding result analysis, aimed at evaluating the performance of the proposed approach were performed.

Artificial perception, in the context of autonomous driving, is the process by which an intelligent system translates sensory data into an effective model of the environment surrounding a vehicle. In this paper, and considering data from a 3D-LIDAR mounted onboard an intelligent vehicle, a 3D perception system based on voxels and planes is proposed for ground modeling and obstacle detection in urban environments. The system, which incorporates time-dependent data, is composed of two main modules: (i) an effective ground surface estimation using a piecewise plane fitting algorithm and RANSAC-method, and (ii) a voxel-grid model for static and moving obstacles detection using discriminative analysis and ego-motion information. This perception system has direct application in safety systems for intelligent vehicles, particularly in collision avoidance and vulnerable road users detection, namely pedestrians and cyclists. Experiments, using point-cloud data from a Velodyne LIDAR and localization data from an Inertial Navigation System were conducted for both a quantitative and a qualitative assessment of the static/moving obstacle detection module and for the surface estimation approach. Reported results, from experiments using the KITTI database, demonstrate the applicability and efficiency of the proposed approach in urban scenarios.