What can be done with an embedded stereo-rig in urban environments?

International audienceThe development of the Autonomous Guided Vehicles (AGVs) with urban applications are now possible due to the recent solutions (DARPA Grand Challenge) developed to solve the Simultaneous Localization And Mapping (SLAM) problem: perception, path planning and control. For the last decade, the introduction of GPS systems and vision have been allowed the transposition of SLAM methods dedicated to indoor environments to outdoor ones. When the GPS data are unavailable, the current position of the mobile robot can be estimated by the fusion of data from odometer and/or Inertial Navigation System (INS). We detail in this article what can be done with an uncalibrated stereo-rig, when it is embedded in a vehicle which is going through urban roads. The methodology is based on features extracted on planes: we mainly assume the road at the foreground as the plane common to all the urban scenes but other planes like vertical frontages of buildings can be used if the features extracted on the road are not enough relevant. The relative motions of the coplanar features tracked with both cameras allow us to stimate the vehicle ego-motion with a high precision. Futhermore, the features which don't check the relative motion of the considered plane can be assumed as obstacles

Asservissement visuel applique a un robot mobile : etat de l'art et modelisation cinematique

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Topological segmentation of indoors/outdoors sequences of spherical views

International audienceTopological navigation consists for a robot in navigating in a topological graph which nodes are topological places. Either for indoor or outdoor environments, segmen- tation into topological places is a challenging issue. In this paper, we propose a common approach for indoor and out- door environment segmentation without elaborating a complete topological navigation system. The approach is novel in that environment sensing is performed using spherical images. Envi- ronment structure estimation is performed by a global structure descriptor specially adapted to the spherical representation. This descriptor is processed by a custom designed algorithm which detects change-points defining the segmentation between topological places

Singularities in the determination of the situation of a robot effector from the perspective view of 3 points

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Bearing-only SLAM: comparison between probabilistic and deterministic methods

This work deals with the problem of simultaneous localization and mapping (SLAM). Classical methods for solving the SLAM problem are based on the Extended Kalman Filter (EKF-SLAM) or particle filter (FastSLAM). These kinds of algorithms allow on-line solving but could be inconsistent. In this report, the above-mentioned algorithms are not studied but global ones. Global approaches need all measurements from the initial step to the final step in order to compute the trajectory of the robot and the location of the landmarks. Even if global approaches do not allow on-line solving, they can be more interesting than EKF-SLAM or FastSLAM since they are less sensitive to inconsistencies. Two algorithms are studied: the GraphSLAM, a probabilistic method based on gaussian hypothesis, and the "interval SLAM" which is a deterministic approach. A comparison of the algorithms is made in simulation, for the bearing-only case. Landmarks are 3D points from which we measure the bearing and elevation angles. The results show the consistency of both algorithms when the errors are centered. In this case, if we look the size of the belief areas provided by the algorithms, GraphSLAM delivers better results than interval SLAM. Finally, the GraphSLAM algorithm becomes inconsistent when input data are biased. In the latter case, interval SLAM gives good and consistent results

Trajectography of an Uncalibrated Stereo Rig in Urban Environments

This paper describes an original method to compute the relative motion of an uncalibrated stereo rig in urban environments from features lying on the road. The extraction of significant reliable features on the road remains the critical step of this method. We nevertheless detect them according to the stereo constraints and an a priori knowledge on the scene. The motion between two frames of the stereo rig is considered as rigid: the homography computation is enforced by the redundancy of the feature locations in multiple views. The method has been tested on video sequences recorded from a test vehicle that was driven in an urban environments. Promising results from these experiments will be presented

Réalisation et calibration d'un système expérimental de vision composé d'une caméra mobile embarquée sur un robot-manipulateur

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Détection robuste du plan de la route en milieu urbain

Cet article décrit un algorithme de segmentation du plan de la route dans des images de scènes urbaines. Nous supposons que l'environnement contient une majorité de contours alignés selon les 3 directions principales. L'algorithme se décompose en deux étapes. La première détecte conjointement les lignes de fuite qui convergent vers le point de fuite dominant. Les lignes de fuite sont issues d'une détection de contours à laquelle nous appliquons des contraintes spécifiques aux scènes urbaines. Nous recherchons alors des points d'intérêts dans la région de l'image délimitée par les lignes de fuite coplanaires. Lors de la deuxième étape, nous mettons en correspondance l'ensemble des caractéristiques détectées (points d'intérêts et lignes de fuite) en utilisant les propriétés d'invariance projective entre deux prises de vues d'une même scène de façon à estimer l'homographie induite par le plan de la route

Homography from a Vanishing Point in Urban Scenes

In this paper, we address the problem of computing the ego-motion of a vehicle in an urban environment using dynamic vision. We assume a planar piecewise world where the planes are mainly distributed along three principal directions corresponding to the axes of a reference frame linked to the ground plane with a vertical z-axis. We aim to estimate both the motion of the car and the principal planes in the scene corresponding to the road and the frontages of the building from a sequence of images provided by an on-board uncalibrated camera. In this paper, we present preliminary results concerning the robust segmentation of the road using projective properties of the scene. We develop a two-stage algorithm in order to increase robustness. The first stage detects the borders of the road using a contour-based approach and primarily allows us to estimate the Dominant Vanishing Point (DVP). The DVP and the borders of the road are then used to constrain the region where the points of interest, corresponding to the road lane markers, can be extracted. The second stage uses a robust technique based on projective invariant to match the lines and points between two consecutive images in the sequence. Finally, we compute the homography relating the points and lines lying on the road into the two images

Increasing the Convergence Domain of RGB-D Direct Registration Methods for Vision-based Localization in Large Scale Environments

International audienceDeveloping autonomous vehicles capable of dealing with complex and dynamic unstructured environments over large-scale distances, remains a challenging goal. One of the major difficulties in this objective is the precise localization of the vehicle within its environment so that autonomous navigation techniques can be employed. In this context, this paper presents a methodology to map building and to efficient pose computation which is specially adapted for cases of large displacements. Our method uses hybrid robust RGB-D cost functions that have different convergence properties, whilst exploiting the visibility rotation invariance given by panoramic spherical images. The proposed registration model is composed of a RGB and point-to-plane ICP cost in a multi-resolution framework. We close up the paper presenting mapping and localization results in real outdoor scenes