Extended occupation grids for non-rigid moving objects tracking

International audienceWe 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 un- structured 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 perfor- mance 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

Proposition for propagated occupation grids for non-rigid moving objects tracking

International audienceAutonomous navigation among humans is, however simple it might seems, a difficult subject which draws a lot a attention in our days of increasingly autonomous systems. From a typical scene from a human environment, diverse shapes, behaviours, speeds or colours can be gathered by a lot of sensors ; and a generic mean to perceive space and dynamics is all the more needed, if not easy. We propose an incremental evolution over the well-known occupancy grid paradigm, introducing grid cell propagation over time and a limited neighbourhood, handled by probabilistic calculus. Our algorithm runs in real-time from a GPU implementation, and considers completely generically space-cells propagation, without any a priori requirements. It produces a set of belief maps of our environment, handling occupancy, but also items dynamics, relative rigidity links, and an initial object classification. Observations from free-space sensors are thus turned into information needed for autonomous navigation

Proposition for propagated occupation grids for non-rigid moving objects tracking

International audienceAutonomous navigation among humans is, however simple it might seems, a difficult subject which draws a lot a attention in our days of increasingly autonomous systems. From a typical scene from a human environment, diverse shapes, behaviours, speeds or colours can be gathered by a lot of sensors ; and a generic mean to perceive space and dynamics is all the more needed, if not easy. We propose an incremental evolution over the well-known occupancy grid paradigm, introducing grid cell propagation over time and a limited neighbourhood, handled by probabilistic calculus. Our algorithm runs in real-time from a GPU implementation, and considers completely generically space-cells propagation, without any a priori requirements. It produces a set of belief maps of our environment, handling occupancy, but also items dynamics, relative rigidity links, and an initial object classification. Observations from free-space sensors are thus turned into information needed for autonomous navigation

Systèmes de perception robustes pour environnements dynamiques : applications aux systèmes d'évitement de piétons

Robots have been given sophisticated ”eyes” to make them ”see” and understand their environments. These eyes (cameras, ladars, sonars, radars, etc...) collect a huge amount of data that need to be correctly processed to be useful. Processing this information is what a perception system is intended to perform. For almost half a century now, various perception algorithms have been proposed to tackle one or several of the underlying issues that arise when addressing the perception problem. Well known tracking, detection, mapping, localization and classification algorithms can consequently be combined to design complete perception algorithms that work well for a given application in most situations. The problem is that some real world applications (autonomous driving, etc...) require perception systems that do better than working well in most situations. An autonomous vehicle driving in a crowded urban center would need indeed to be equipped with a perception system that works well in every situation. This dissertation addresses the specific problem of perception systems reliability when confronted to highly changing dense environment. First a detailed analysis of the fundamental limitations undermining the performances of existing approaches is given. Then an original approach - based on a unified grid-based formulation of the five perceptual subproblems - is proposed and proves to be capable of solving issues that most existing systems cannot solve. The relevance of this analysis and the experimental validity of the proposed approach is assessed through an experimental comparison of two fully detailed original perception systems specifically designed for pedestrian detection purposes in urban environments.La plupart des systèmes robotiques sont équipés de capteurs sophistiqués censés leur donner la capacité de « voir » et en conséquence de comprendre l'environnement dans lequel ils évoluent. Cependant, la quantité impressionnante d'information que ces capteurs collectent régulièrement n'est réellement mise à profit que si le robot qui en est possède la capacité de les traiter correctement. Depuis plusieurs décennies, une grande variété d'algorithmes de perception a été proposée à cet effet. Il est donc déjà possible d'assembler des algorithmes bien connus de détection, de pistage, de classification, de cartographie et de localisation pour concevoir des systèmes de perception complets capables d'opérer pour une application donnée, dans la plupart des situations. Malheureusement, un certain nombre d'applications concrètes exigent des systèmes de perception qui font bien mieux que de fonctionner la plupart du temps. Un véhicule automatique (sans conducteur) évoluant dans un centre ville ne pourra par exemple se satisfaire que d'un système de perception qui fonctionne dans toutes les situations. Ce mémoire de thèse trait précisément de fiabilité inhérente aux systèmes de perception actuels lorsqu'ils sont confrontés à des environnements complexes et changeants. Une analyse détaillée des causes de ce manque de fiabilité est d'abord proposée. Nous proposons et d'écrivons ensuite une approche nouvelle du problème de perception basée sur une formulation unifiée de ses cinq problèmes sous-jacents (détection, pistage, classification, cartographie et localisation). Nous montrons ainsi que cette approche permet de contourner naturellement les difficultés qui bornent les performances de la plupart des systèmes existants. La pertinence de l'analyse présentée dans ce document ainsi que la validité expérimentale des solutions proposées sont évaluées au travers d'une comparaison concrète entre deux systèmes de perception originaux conçus pour « percevoir» des piétons en environnement urbain