Michel Devy

Undelayed initialization in bearing only SLAM

Most solutions to the SLAM problem in robotics have utilised range and beating sensors as the provided perception data is easy to incorporate, allowing immediate landmark initialization. This is not the case when using bearing-only information because the distance to the perceived landmarks is not directly provided. A whole estimate of a landmark position is only possible via a set of measurements taken from different points of view. The vast majority of contributions to this problem perform a parallel task to get this estimate, and hence the landmark initialization is delayed. We give a new insight to the problem and present a method to avoid this delay by initializing the whole ray that defines the direction of the landmark. We utilize a minimal and computationally efficient form to represent this ray and a new strategy for the subsequent updates. Simulations have been carried out to validate the proposed algorithms.

Fast range image segmentation by an edge detection strategy

This paper presents an edge-based segmentation technique that allows to process quickly very large range images. The proposed technique consists of two stages. First, a binary edge map is generated; then, a contour detection strategy is responsible for the extraction of the different boundaries. The first stage generates a binary edge map based on a scan line approximation technique. There is a difference with the previous techniques, as only two orthogonal scan line direction are considered. The planar curves defined by the elements contained in each scan line are approximated by oriented quadratic curves. The representative points from each curve are used to define a binary edge map. The second stage is a new approach to the classical contour extraction problem. It shows a difference with the previous approaches which use the enclosed surface information; with the suggested technique, boundaries are obtained by using only the information contained in the binary edge map. It consists in linking the edge points by applying a graph strategy. Experimental results with large panoramic range images are presented.

Fusing Monocular Information in Multicamera SLAM

This paper explores the possibilities of using monocular simultaneous localization and mapping (SLAM) algorithms in systems with more than one camera. The idea is to combine in a single system the advantages of both monocular vision (bearings-only, infinite range observations but no 3-D instantaneous information) and stereovision (3-D information up to a limited range). Such a system should be able to instantaneously map nearby objects while still considering the bearing information provided by the observation of remote ones. We do this by considering each camera as an independent sensor rather than the entire set as a monolithic supersensor. The visual data are treated by monocular methods and fused by the SLAM filter. Several advantages naturally arise as interesting possibilities, such as the desynchronization of the firing of the sensors, the use of several unequal cameras, self-calibration, and cooperative SLAM with several independently moving cameras. We validate the approach with two different applications: a stereovision SLAM system with automatic self-calibration of the rigs main extrinsic parameters and a cooperative SLAM system with two independent free-moving cameras in an outdoor setting.

Incremental construction of a landmark-based and topological model of indoor environments by a mobile robot

This paper deals with the perception subsystem of a mobile robot which must navigate in a structured environment. We will consider the exploration task; the robot must build successive snapshot models from sensory data acquired from a laser range finder (LRF), and fuse them in a global model so that it can localize itself with respect to a pertinent reference frame. Structuration rules are required to limit the complexity of the modeling process. First of all, we only extract from the sensory data, useful landmarks which correspond to characteristic local feature groupings, like wall corners, doors, corridor crossings, ...; each landmark has its own frame and its own geometrical model. Then, correlated landmarks are kept together at an area level; finally, the global model is built from the relationships between area frames, providing the topological description of the world. For each level (landmark, area, environment), the model is represented by a random vector and a covariance matrix, updated through the use of an extended Kalman filter (EKF).

Uncertain map making in natural environments

Building on previous work on incremental natural scene modelling for mobile robot navigation, we focus in this paper on the problem of representing and managing uncertainties. The environment is composed of ground regions and objects. Objects (e.g., rocks) are represented by an uncertain state vector (location) and a variance-covariance matrix. Their shapes are approximated by ellipsoids. Landmarks are defined as objects with specific properties (discrimination, accuracy) that permit to use them for robot localization and for anchoring the environment model. Model updating is based on an extended Kalman filter. Experimental results are given that show the construction of a consistent model over tens of meters.

Detection and classification of passenger seat occupancy using stereovision

Vision systems offer new opportunities for the improvement of vehicle safety. The detection and classification of passenger seat occupancy open up new ways to control the airbag firing. We present a stereo system designed for the observation of the cockpit scene in order to provide information about the passenger presence and location within the vehicle cockpit; from the stereo data, a cockpit occupancy map is generated. Several typical configurations of the passenger seat must be recognized (empty seat, adult presence, baby seat, ...). During an offline learning step, several cockpit images are recorded for each of these situations; for each one discriminant attributes are extracted. Then, the seat situation is recognized using a case-based classification method.

A visual landmark framework for mobile robot navigation

This article describes visual functions dedicated to the extraction and recognition of visual landmarks, here planar quadrangles detected by a single camera. Landmarks are extracted among edge segments through a relaxation scheme, used to apply geometrical, topological and appearance constraints on sets of segments. Once extracted, such a landmark is characterized by invariant attributes so that recognition is made possible from a large range of viewpoints. Landmarks are represented by an icon which is built using the homography between the current viewpoint and a reference shape (a square). When detected again, the landmark is recognized by using a distance between icons. We propose a comparison of several of these metrics and an evaluation on actual and synthetic images that shows the validity of our approach. Results issued from experiments of a mobile robot navigating in an indoor environment are finally presented.

Quality Enhancement of Reconstructed 3D Models Using Coplanarity and Constraints

We present a process to improve the structural quality of automatically acquired architectural 3D models. Common architectural features like orientations of walls are exploited. The location of these features is extracted by using a probabilistic technique (RANSAC). The relationships among the features are automatically obtained by labelling them usinga semantic net of an architectural scene. An evolutionary algorithm is used to optimise the orientations of the planes. Small irregularities in the planes are removed by projecting the triangulation vertices onto the planes. Planes in the resulting model are aligned to each other. The technique produces models with improved appearance. It is validated on synthetic and real data.

A visual landmark framework for indoor mobile robot navigation

Presents vision functions needed on a mobile robot to deal with landmark-based navigation in buildings. Landmarks are planar, quadrangular surfaces, which must be distinguished from the background, typically a poster on a wall or a door-plate. In a first step, these landmarks are detected and their positions with respect to a global reference frame are learned; this learning step is supervised so that only the best landmarks are memorized, with an invariant representation based on a set of interest points. Then, when the robot looks for visible landmarks, the recognition procedure takes advantage of the partial Hausdorff distance to compare the landmark model and the detected quadrangles. The paper presents the landmark detection and recognition procedures, and discusses their performances.

Visual landmarks detection and recognition for mobile robot navigation

This article describes visual functions dedicated to the extraction and recognition of planar quadrangles detected from a single camera. Extraction is based on a relaxation scheme with constraints between image segments, while the characterization we propose allows recognition to be achieved from different view-points and viewing conditions. We defined and evaluated several metrics on this representation space - a correlation-based one and another one based on sets of interest points.