Title: Static Free Space Detection with Laser Scanner using Occupancy Grid Maps,
Abstract: Drivable free space information is vital for autonomous vehicles that have to
plan evasive maneuvers in real-time. 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.