Visibility Contractors: Application to Mobile Robot Localization

Visibility is studied and used in several fields: computer graphics, telecommunication, robotics... For instance, in Computer-aided design (CAD) synthesis images are created by simulating light propagation in a scene. Visibility notions are then necessary to compute the visible objects from a point of view, and the shadow of those objects. In mobile robotics the visibility is used for path planning (visibility graph) and localization problems. This presentation is about visibility information for mobile robot localization. The objective is twofold. First a visibility notion based on segment intersections is presented. By considering a set-membership approach it is possible to develop contractors associated to this visibility relation. Then two applications of those visibility contractors to mobile robot localization are presented. The first one corresponds to the pose tracking of a team of robots. The idea is to use a Boolean information (the visibility between two robots: two robots are visible or not) in order to avoid the drifting of those robots (in order to maintain the precision of their position estimations). The second application corresponds to the processing of an original constraint for a set-membership global localization algorithm. This global localization algorithm is based on a CSP approach (Constraint Satisfaction Problem). Adding a visibility constraint to this CSP improves the accuracy of the algorithm

Interval Analysis for Kidnapping Problem using Range Sensors

This paper presents a new method to deal with thekidnapping problem of mobile robots. By using a range sensor and a discrete map of the indoor environment, the robot has to determine its pose (position and orientation). The idea is to obtain the smallest set of feasible poses compatible with the mesurements and the map. This method is a set membership approach based on interval analysis and constraint propagation, which allows to get results in a guaranteed way

Model-based approach for fault diagnosis using set-membership formulation

This paper describes a robust model-based fault diagnosis approach that enables to enhance the sensitivity analysis of the residuals. A residual is a fault indicator generated from an analytical redundancy relation which is derived from the structural and causal properties of the signed bond graph model. The proposed approach is implemented in two stages. The first stage consists in computing the residuals using available input and measurements while the second level leads to moving horizon residuals enclosures according to an interval consistency technique. These enclosures are determined by solving a constraint satisfaction problem which requires to know the derivatives of measured outputs as well as their boundaries. A numerical differentiator is then proposed to estimate these derivatives while providing their intervals. Finally, an inclusion test is performed in order to detect a fault upon occurrence. The proposed approach is well suited to deal with different kinds of faults and its performances are demonstrated through experimental data of an omni-directional robot

LiDAR-only based navigation algorithm for an autonomous agricultural robot

The purpose of the work presented in this paper is to develop a general and robust approach for autonomous robot navigation inside a crop using LiDAR (Light Detection And Ranging) data. To be as robust as possible, the robot navigation must not need any prior information about the crop (such as the size and width of the rows). The developed approach is based on line extractions from 2D point clouds using a PEARL based method. In this paper, additional filters and refinements of the PEARL algorithm are presented in the context of crop detection. A penalization of outliers, a model elimination step, a new model search and a geometric constraint are proposed to improve the crop detection. The approach has been tested over a simulator and compared with classical PEARL and RANSAC based approaches. It appears that adding those modification improved the crop detection and thus the robot navigation. Those results are presented and discussed in this paper. It can be noticed that even if this paper presents simulated results (to ease the comparison with other algorithms), the approach also has been successfully tested using an actual Oz weeding robot, developed by the French company Naio Technologies

Technical Report - Visibility Contractors

Technical report about the visibility contractors (propositions, proofs, algorithms)

Diagnostic à base de modèles et aide à la prise de décision robuste par une approche ensembliste

L\u27un des enjeux les plus importants des technologies impliquées dans l\u27ingénierie des systèmes complexes concerne aujourd\u27hui le diagnostic temps réel. Cette discipline repose principalement sur les algorithmes de détection et de localisation de défauts. Dans le présent papier, nous présentons une méthode générique permettant d\u27améliorer la robustesse de la procédure de détection de défauts. Cette méthode procède en deux étapes distinctes. Dans un premier temps, l\u27approche des Bond Graphs est utilisée pour générer, sur la base d\u27un modèle graphique, un ensemble d\u27indicateurs de défauts appelés résidus. Dans un second temps, les seuils de détectabilité permettant d\u27évaluer ces résidus sont déterminés grâce à l\u27analyse par intervalles et aux techniques de satisfaction de contraintes dans le but de réduire au maximum le taux de fausses alarmes et de non détection. Les performances de la méthode proposée sont démontrées par des données expérimentales provenant d\u27un robot omnidirectionnel

Cart-O-matic project : autonomous and collaborative multi-robot localization, exploration and mapping

International audienceThe aim of the Cart-O-matic project was to design and build a multi-robot system able to autonomously map an unknown building. This work has been done in the framework of a French robotics contest called Defi CAROTTE organized by the General Delegation for Armaments (DGA) and the French National Research Agency (ANR). The scientific issues of this project deal with Simultaneous Localization And Mapping (SLAM), multi-robot collaboration and object recognition. In this paper, we will mainly focussed on the two first topics : after a general introduction, we will briefly describe the innovative simultaneous localization and mapping algorithm used during the competition. We will next explain how this algorithm can deal with multi-robots systems and 3D mapping. The next part of the paper will be dedicated to the multi-robot pathplanning and exploration strategy. The last section will illustrate the results with 2D and 3D maps, collaborative exploration strategies and example of planned trajectories

Guaranteed Interval Analysis Localization for Mobile Robots

International audienceThis paper presents a set membership method (named Interval Analysis Localization (IAL)) to deal with the global localization problem of mobile robots. By using a LIDAR (LIght Detection And Ranging) range sensor, the odometry and a discrete map of an indoor environment, a robot has to determine its pose (position and orientation) in the map without any knowledge of its initial pose. In a bounded error context, the IAL algorithm searches a set of boxes (interval vector), with a cardinality as small as possible that includes the robot’s pose. The localization process is based on constraint propagation and interval analysis tools, such as bisection and relaxed intersection. The proposed method is validated using real data recorded during the CAROTTE challenge, organized by the French ANR (National Research Agency) and the French DGA (General Delegation of Armament). IAL is then compared with the well-known Monte Carlo Localization showing weaknesses and strengths of both algorithms. As it is shown in this paper with the IAL algorithm, interval analysis can be an efficient tool to solve the global localization problem

An Image Processing Method Based on Features Selection for Crop Plants and Weeds Discrimination Using RGB Images

In the context of computer vision applied to precision agriculture, this paper presents an imaging system based on shape and intensity features, extracted from RGB images, for the discrimination between crop plants and weeds. A segmentation method with many constraints to overcome light acquisition conditions is used and coupled with morphological filtering suitable for denoising segmented images. A SVMs classifier based on a polynomial kernel function is implemented and a k-folds cross validation process is used to evaluate the performance of the SVMs classifier usable in 2 different configurations. On a training dataset, these 2 configurations are evaluated for the performance of classification in terms of true and false positive rates, according to ROC curves and area under curves. On a test dataset, these 2 configurations are exploited, giving both a relevant classification rate