Extended occupation grids for non-rigid moving objects tracking

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. I. OCCUPANCY GRIDS EXTENDED To begin with, we present in this part our motivations for this work, and some limits of current algorithms for the specific task which we aim to deal with. A comeback to the occupancy grid paradigm is our next point, before some thoughts about what could be an optimal occupancy grid update rule, and why its practical use is limited. A second part is devoted to our proposition, trying to tackle some of the observations previously made while dealing with some approximations and limits inherent to practical realisation. A last small part presents some performance considerations, which are important for scalability issues in case of collaborative work, and expected evolution with new computing hardware. A. General considerations 1) Perception of non-structured moving objects: Amid a vast set of localisation and perception algorithms currently developed, among which various SLAM have taken the greater share for the last years, some challenges are still untackled in the perception field. Detection and tracking of moving objects, without pre-requisites on rigidity, structured environment or immobility remains a tough question, and is not yet addressed by many approaches. Different SLAMbased approaches have been used for the last years, some of them being very successful in building a map of the environment from a non-associative sensor while dealing with the identification of some moving objects amid the map. FastSLAM, using particle filtering and initially presented by Thrun et. al ([1]) or other iterative optimisation techniques (ICP, [2] [3]) are very powerful algorithms which can deal with some level of movement amid tracked points, as long as fixed points of the map are enough to estimate robot pose and environment mapping, and to isolate non conforming elements. This proved effective, for example by Wang et. al ([4]), in constrained cases where the number of moving parts remains low. Overall scene requisites are indeed strong using these techniques, and would probably not be enough for reliable moving object perception and tracking in a highly unstructured environment. Contrary to SLAM, our aim is thus not primarily to localise oneself in the environment along with building a map of its static features, but instead to be able to reliably detect and monitor position and dynamics of surrounding moving objects, possibly outdoor and without much visible solid infrastructure. 2) Interests and limits of the simple grid-based approach: Occupancy grids are very common in robotics, since their first introduction by Moravec and Elfes ([5]), initially related to sonar based mapping. Principles are simple and effective, relying on the information storage and information source spatial localisation similarities, thus allowing to keep any spatial relations between cells at a minimal cost. Occupancy grids were thus at the heart of most of the latests SLAM propositions (based on filtering or optimisation), and in many of broadly speaking perception systems (Badino et al [6] showed for example a promising free-space perception system based on vision and occupancy grids in 2007). Additional processing of information is present in those examples, due to the very limited nature of conventional grids data integration, usually relying on large number law for any kind of temporal filtering. This is one of the major approximations chosen in most occupancy grids to limit computational complexity, at the expense of information loss. Another major approximation historically in the heart of occupancy grids is the cell independence pre-requisite, which greatly simplifies eventual probabilities calculus. Obviously a big step from accurate world description, this is basically of little consequences in case the aim is, as it was in the original Moravec article [5], the cartography of still environment. In case we chose the occupancy grid formalism to track moving objects, this is however a major limitation, as one could ha l-0 06 62 17 5, v er si on 1 23 J an 2 01 2 Author manuscript, published in "Intelligent Transportation Systems (ITSC), 2011 14th International IEEE Conference on (2011)"