Multi-Layer Laser Scanner Strategy for Obstacle Detection and Tracking

Abstract

This paper deals with real-time obstacle detection and tracking using multi-layer LIDAR data. We present two algorithms to cluster raw data coming from LIDAR sensors. The first algorithm is based on a dynamic clustering approach while the second one relies on the connectivity between the laser impacts. Both algorithms take into account the inaccuracy and the uncertainty of the data sources. We propose a tracking approach based on the belief theory to estimate the dynamic state of the detected objects in order to predict their future maneuvers. The objects are then filtered using an intelligent ROI that depends on a dynamic evolution area computed from proprioceptive information of the ego-vehicle. We evaluate and validate the whole chained process on real data-sets.