Extended occupation grids for non-rigid moving objects tracking

Abstract

We present an evolution of traditional occupancy grid algorithm, based on an extensive probabilistic calculus of the evolution of several variables on a cell neighbourhood. Occupancy, speed and classification are taken into account, the aim being to improve overall perception of an highly changing unstructured environment. Contrary to classical SLAM algorithms, no requisite is made on the amount of rigidity of the scene, and tracking do not rely on geometrical characteristics. We believe that this could have important applications in the automotive field, both from autonomous vehicle and driver assistance, in some areas difficult to address with current algorithms. This article begins with a general presentation of what we aim to do, along with considerations over traditional occupancy grids limits and their reasons. We will then present our proposition, and detail some of its key aspects, namely update rules and performance consequences. A second part will be more practical, and will begin with a brief presentation of the GPU implementation of the algorithm, before turning to sensor models and some results.

An evidential sensor model for Velodyne scan grids

“…Many extensions have been published in the literature, like the Bayesian Occupancy Filter (BOF) [1] which estimates both the occupancy and the speed of the cells. [2] proposed an extended occupancy grid approach which can be used to track non-rigid moving objects. [3] applied the bayesian occupancy grid map to detect road boundaries.…”