Application of the LIDAR technology for obstacle detection during the operation of agricultural vehicles

. 2013. Application of the LIDAR technology for obstacle detection during the operation of agricultural vehicles. Canadian Biosystems Engineering/Le génie des biosystèmes au Canada 55: 2.9-2.16. Many algorithms have been proposed in the literature for the detection of foreign objects or obstacles to the operation of autonomous vehicles. However, a comparative evaluation of these existing approaches is still lacking. In this study, multiple feature recognition algorithms (average height, density, connectivity, and discontinuity methods) were evaluated for the identification of three types of foreign objects placed in four types of crops (range of crop height: 20 -80 cm) under different field and operating conditions. The field experiments were completed using a SICK laser measurement system (LMS) 291-S14 scanner that was placed on a tractor to scan standing crops in which the standard test objects had been placed. The data collected by the sensor was analyzed using the software MATLAB 2D and 3D versions. The average height method allowed for a 72.4% average object detection rate while the connectivity method only resulted in a successful object detection rate of 18% for all the experiments. It was also found that the crop density or foliage cover had a negative impact on the detection rate for shorter test objects with the higher rates of obstacle detection being achieved for objects significantly taller than crops. Increasing vehicle speed was also found to reduce detection abilities due to lower scan resolution per distance travelled. Keywords: Laser Measurement System, object detection, crops, foreign objects, autonomous vehicles, safety. Plusieurs algorithmes ont été développés pour déterminer la position d'objets pouvant entraver le fonctionnement de véhicules autonomes. Il reste cependant à compléter une évaluation comparative de ces différentes approches. Dans le cadre de cette étude, l'efficacité de quatre algorithmes de reconnaissance et de détection d'obstacles (méthodes basées sur la hauteur moyenne, la densité, la connectivité et la discontinuité) pour la détection de trois types d'obstacles placés dans quatre types de récolte différentes (gamme de hauteur des plantes : 20 -80 cm) a été comparée pour différentes combinaisons de conditions d'opération au champ. Les tests ont été complétés à l'aide d'un capteur SICK de mesure au laser (LMS) 291-S14 installé sur un tracteur agricole afin de détecter des objets placés à des endroits prédéterminés à proximité de la trajectoire du tracteur. Les données recueillies par le capteur ont été analysées à l'aide du logiciel MATLAB (versions 2D et 3D). La méthode de la hauteur moyenne a permis d'atteindre un taux de détection global des obstacles au champ de 72,4% alors que la méthode de la connectivité a fourni les résultats les moins intéressants avec un taux de détection global de seulement 18%. Les résultats obtenus indiquent également que la taille des cultures ainsi que la densité du couvert végétal ont résulté en des taux de détection des obstacles moins élevés. Les taux de détection ont également été moins élevés dans le cas des obstacles de petite taille alors que les taux de détection des objets dont la hauteur dépasse celle des cultures étaient plus élevés. L'accroissement de la vitesse d'avancement du tracteur a eu un impact négatif sur le taux de détection des obstacles. Mots-clés: système de détection laser, détection d'obstacles, cultures, véhicule autonome, sécurité

Credibilist Simultaneous Localization and Mapping with a LIDAR

International audienceFrom the early beginning, the Simultaneous Localization And Mapping (SLAM) problem has been approached using a probabilistic background. A new solution based on the Transferable Belief Model (TBM) framework is proposed in this article. It appears that this representation of knowledge affords numerous advantages over the classic probabilistic ones and leads to particularly good performances (an average of 3.2% translation drift and 0.0040deg/m rotation drift), especially when it comes to crowded environment. By introducing the basic concepts of a Credibilist SLAM, this article aims at proving that the use of this new theoretical context opens a lot of perspectives for the SLAM community.Dès le départ, la problématique de localisation et cartographie simultanée (SLAM) a été approchée avec un contexte probabiliste. Une nouvelle solution basée sur les modèles de croyance transférable (TBM) est proposée dans cet article. Ce type de représentation de la connaissance s'avère avantageux en comparaison aux probabilités et conduit à de particulièrement bonnes performances (une moyenne de 3,2% en dérive en translation et de 0,0040 deg/m en dérive en rotation), spécialement pour les environnements encombrés. En introduisant les concepts de base d'un SLAM crédibiliste, cet article tente de prouver que l'utilisation de ce nouveau contexte théorique ouvre de nombreuses perspectives pour la communauté du SLA

HMM-Based Dynamic Mapping with Gaussian Random Fields

This paper focuses on the mapping problem for mobile robots in dynamic environments where the state of every point in space may change, over time, between free or occupied. The dynamical behaviour of a single point is modelled by a Markov chain, which has to be learned from the data collected by the robot. Spatial correlation is based on Gaussian random fields (GRFs), which correlate the Markov chain parameters according to their physical distance. Using this strategy, one point can be learned from its surroundings, and unobserved space can also be learned from nearby observed space. The map is a field of Markov matrices that describe not only the occupancy probabilities (the stationary distribution) as well as the dynamics in every point. The estimation of transition probabilities of the whole space is factorised into two steps: The parameter estimation for training points and the parameter prediction for test points. The parameter estimation in the first step is solved by the expectation maximisation (EM) algorithm. Based on the estimated parameters of training points, the parameters of test points are obtained by the predictive equation in Gaussian processes with noise-free observations. Finally, this method is validated in experimental environments

Survey of Robot 3D Path Planning Algorithms

Robot 3D (three-dimension) path planning targets for finding an optimal and collision-free path in a 3D workspace while taking into account kinematic constraints (including geometric, physical, and temporal constraints). The purpose of path planning, unlike motion planning which must be taken into consideration of dynamics, is to find a kinematically optimal path with the least time as well as model the environment completely. We discuss the fundamentals of these most successful robot 3D path planning algorithms which have been developed in recent years and concentrate on universally applicable algorithms which can be implemented in aerial robots, ground robots, and underwater robots. This paper classifies all the methods into five categories based on their exploring mechanisms and proposes a category, called multifusion based algorithms. For all these algorithms, they are analyzed from a time efficiency and implementable area perspective. Furthermore a comprehensive applicable analysis for each kind of method is presented after considering their merits and weaknesses