Static Free Space Detection with Laser Scanner using Occupancy Grid Maps

Drivable free space information is vital for autonomous vehicles that have to plan evasive maneuvers in realtime. In this paper, we present a new efficient method for environmental free space detection with laser scanner based on 2D occupancy grid maps (OGM) to be used for Advanced Driving Assistance Systems (ADAS) and Collision Avoidance Systems (CAS). Firstly, we introduce an enhanced inverse sensor model tailored for high-resolution laser scanners for building OGM. It compensates the unreflected beams and deals with the ray casting to grid cells accuracy and computational effort problems. Secondly, we introduce the ‘vehicle on a circle for grid maps’ map alignment algorithm that allows building more accurate local maps by avoiding the computationally expensive inaccurate operations of image sub-pixel shifting and rotation. The resulted grid map is more convenient for ADAS features than existing methods, as it allows using less memory sizes, and hence, results into a better real-time performance. Thirdly, we present an algorithm to detect what we call the ‘in-sight edges’. These edges guarantee modeling the free space area with a single polygon of a fixed number of vertices regardless the driving situation and map complexity. The results from real world experiments show the effectiveness of our approach. Keywords— Occupancy Grid Map; Static Free Space Detection; Advanced Driving Assistance Systems; laser scanner; autonomous driving