Ouiddad Labbani-Igbida

Circle formation of weak mobile robots

We consider distributed systems made of <i>weak mobile</i> robots, that is, mobile devices, equipped with sensors, that are <i>anonymous</i>, <i>autonomous</i>, <i>disoriented</i>, and <i>oblivious</i>. The <i>Circle Formation Problem</i> (CFP) consists of the design of a protocol insuring that, starting from an initial arbitrary configuration where no two robots are at the same position, all the robots eventually form a <i>regular n-gon</i>—the robots take place on the circumference of a circle <i>C</i> with equal spacing between any two adjacent robots on <i>C</i>. CFP is known to be unsolvable by arranging the robots evenly along the circumference of a circle <i>C</i> without leaving <i>C</i>—that is, starting from a configuration where the robots are on the boundary of <i>C</i>. We circumvent this impossibility result by designing a scheme based on <i>concentric circles</i>. This is the first scheme that deterministically solves CFP. We present our method with two different implementations working in the semi-synchronous system (SSM) for any number <i>n</i> ≥ 5 of robots.

Deterministic Robot-Network Localization is Hard

This paper provides a complexity study of the deterministic localization problem in robot networks using local and relative observations only. This is an important issue in collective and cooperative robotics where global positioning systems (GPS) are not available, and the basic premise is the localization ability of the group. We prove that given a set of relative observations made by the robots, the unique unambiguous pose estimation of the robot network in a deterministic way is an NP-hard problem. This means that no polynomial-time algorithm can deterministically solve the unique pose estimation problem based on relative observations unless P=NP. The consequence is that no guarantee can be provided, in a polynomial time, that the possibly estimated poses of the robots will correspond to the effective (actual) ones. The proof is based on complexity theory where we build appropriate polynomial-time reductions interrelating the multirobot localization problem to a well-known NP-complete problem (the partition problem). This NP -hardness result opens questions and perspectives for research into approximations to overcome its intractability.

Real-time free space detection and navigation using omnidirectional vision and parametric and geometric active contours.

This paper contributes to adapt parametric and geometric active contour methods in a new framework to handle real time free space extraction while taking advantage of the properties of omnivision. Both methods were formally and algorithmically adapted and improved. Some comparative results, achieved on unknown indoor and outdoor images, are presented to validate the efficiency of our two snake based approaches. We also show that active contours can be applied to make a robot navigate autonomously, only using real omni-images, thanks to the extracted free space skeleton.

Robust free space segmentation using active contours and monocular omnidirectional vision

In this paper, we propose a robust and fast active contour based method to free space detection in omnidirectional images where the problem of falsely detected obstacles is solved. We define a new functional energy formulation including altitude estimation of keypoints extracted nearby the active contour modeling the free space. The free space, so extracted, could help the robot in real time navigation and environment exploration tasks. To validate the efficiency of the proposed approach, the paper shows comparative results achieved with a classical formulation and our formulation of active contour energies, using images acquired by a robot exploring unknown indoor and outdoor environments, with no prior knowledge of the shape or the extend of the free space.

Haar invariant signatures and spatial recognition using omnidirectional visual information only

This paper describes a method for spatial representation, place recognition and qualitative self-localization in dynamic indoor environments, based on omnidirectional images. This is a difficult problem because of the perceptual ambiguity of the acquired images, and their weak robustness to noise, geometrical and photometric variations of real world scenes. The spatial representation is built up invariant signatures using Invariance Theory where we suggest to adapt Haar invariant integrals to the particular geometry and image transformations of catadioptric omnidirectional sensors. It follows that combining simple image features in a process of integration over visual transformations and robot motion, can build discriminant percepts about robot spatial locations. We further analyze the invariance properties of the signatures and the apparent relation between their similarity measures and metric distances. The invariance properties of the signatures can be adapted to infer a hierarchical process, from global room recognition to local and coarse robot localization.The approach is validated in real world experiments and compared to some local and global state-of-the-art methods. The results demonstrate a very interesting performance of the proposed approach and show distinctive behaviors of global and local methods. The invariant signature method, while being very time and memory efficient, provides good separability results similarly to approaches based on local features.

Free space detection using active contours in omnidirectional images

Omnidirectional catadioptric cameras offer a large field of view and a complete information about the world surrounding the robot. In this paper, we describe a method to perform a fast and robust extraction of the omnidirectional free space using active contour models. The extracted free space could help the robot in real time navigation and environment exploration tasks. The presented approach will be compared to classical active contour methods usually applied to object segmentation. Some comparative results achieved in indoor and outdoor environments are shown to validate the approach.

Extraction of haar integral features on omnidirectional images: application to local and global localization

In this paper, we present a new method for producing omnidirectional image signatures that are purposed to localize a mobile robot in an office environment. To solve the problem of perceptual aliasing common to the image based recognition approaches, we choose to build signatures that greatly vary between rooms and slowly vary inside a given room. We suggest an averaging technique based on Haar integral invariance. It takes into account the movements the robot can do in a room and the omni image transformations thus produced. The variability of the built signatures is adjusted (total or partial Haar invariance) according to defined subsets of the group transformation. The experimental results prove to get significantly interesting results for place recognition and robot localization with variable accuracy: From global rough localization to local precise one.

The delta-medial axis: A robust and linear time algorithm for Euclidian skeleton computation

Medial axes are known to be very sensitive to shape irregularities. In this paper, we develop a solution to compute a stable medial axis of noisy discrete shapes. It introduces a parameter up to which a deformation (noise) of the shape is considered irrelevant, and thus ignored in the discrete Euclidian Medial Axis computation. We show the linearity property of the proposed algorithm and compare it with two recent state of the art methods: The Gamma Integer Medial Axis and the Discrete Linear Lambda Medial Axis using a single pruning parameter. Based on Kimias database (216 binary images), we present comparative experimental results with respect to skeletonization quality, noise sensitivity and computation time.

On building omnidirectional image signatures using haar invariant features: application to the localization of robots

In this paper, we present a method for producing omnidirectional image signatures that are purposed to localize a mobile robot in an office environment. To solve the problem of perceptual aliasing common to the image based recognition approaches, we choose to build signatures that greatly vary between rooms and slowly vary inside a given room. To do so, an invariant approach has been developed, based on Haar invariant integrals. It takes into account the movements the robot can do in a room and the omni image transformations thus produced. A comparison with existing methods is presented using the Fisher criterion. Our method appears to get significantly better results for place recognition and robot localization, reducing in a positive way the perceptual aliasing.

Qualitative localization using omnidirectional images and invariant features

The present study proposes an innovative approach to qualitative mobile robots localization using the concept of integral invariant on omnidirectional images. They are invariant depending on the image transformations caused by the movements of the robot. Several methods have been suggested to construct such invariants but they often rely on hypotheses about the transformation group which do not hold any more when dealing with omnidirectional sensors. These sensors benefit from an increasing interest in mobile robotics because of their field of view but they require adaptations of classical methods. This paper presents a method based on group averaging to construct invariant features which could be used to recognize a place with this type of sensors.