The ICP (iterative closest point) algorithm remains a very popular method for the registration of 3D data sets, when an initial guess of the relative pose between them is known. The purpose of the work presented in this paper is to improve performance of classical ICP. We address, here, the problem of pair-wise registration of color range images. Many variants of ICP have been proposed for the registration of 3D data sets. However, there are only a few solutions dealing with color range images. In this paper, we have adapted some variants of ICP to take into account color information in the closest point research. Two approaches of color data integration have been evaluated with real 3D/color data sets

This paper shows how interval analysis can be used to get guaranteed 3D measurements with a stereovision sensor. We propose to describe coordinates of image points by intervals. Thus, we propose a new method based on interval analysis tools to propagate this kind of unknown but bounded uncertainty. Finally, we apply this method to compute a guaranteed model for a projective camera and a guaranteed 3D reconstruction.

This paper introduces a decoupled approach of concurrent mapping and localization for mobile robots. Its theoretical aspects rely on recent techniques for correct uncertainty handling using stochastic models: covariance intersection and unscented transform. Further, stochastic constraints are considered as a way to minimize map incoherence with respect to the real environment. Experimental results obtained from multisensory data acquired in a large real environment illustrate the performance of the proposed method.

We propose a weighted least-squares (WLS) algorithm for optimal pose estimation of mobile robots using geometrical maps as environment models. Pose estimation is achieved from feature correspondences in a nonlinear framework without linearization. The proposed WLS approach yields optimal estimates in the least-squares sense, is applicable to heterogeneous geometrical features decomposed in points and lines, and has an O(N) computation time.

This paper deals with main apparent motion estimation using a fuzzy representation of a pixel's gray level. Rough histograms and possibility theory are used to provide an accurate estimation of the motion. The use of these theories allows us to partly cope with the limitations of classical motion estimation methods. This article explains the estimation process in detail and also presents some illustrative examples.

Theoretical solutions based on the matching of 2D range measurements with a map of the environment have been proposed to solve the robot localization problem. However most of them have not been experimented with in real conditions: the robot was stopped or it moved slowly during range data acquisition, and the environment was supposed to be static. We propose and evaluate a dynamic localization method based on feature matching. Experiments carried out in real cluttered indoor environments including people and unknown obstacles show the good performance of the proposed algorithm against the classical solution based on Kalman filtering.

This paper presents a segmentation method for line extraction in 2D range images. It uses a prototype-based fuzzy clustering algorithm in a split-and-merge framework. The split-and-merge structure allows one to use the fuzzy clustering algorithm without any previous knowledge on the number of prototypes. This algorithm aims to be used in mobile robots navigation systems for dynamic map building. Simulation results show its good performance compared to some classical approaches.

Applied statistics are widely used in pattern recognition and other computing applications to find the most likely value of a parameter. The use of classical empirical statistics is based upon assumption about normality of underlying density distribution of data. When the data is corrupted by contaminated noise, then classical tools are usually not robust enough and the estimation of the mode is biased. In this article, we propose to estimate the main mode of a distribution by means of a rough histogram and we show that this estimation is robust to contamination.

This article describes an iterative algorithm for relative motion estimation from 2D range images. The matching is achieved in the geometric feature domain represented by straight lines and ellipsoidal clusters. Using infinite length straight lines instead of line segments as features, the algorithm attempts to achieve robustness to partial occlusions. The motion estimates and the feature correspondence measures are determined in order to minimize a cost function. The performance of this algorithm was evaluated with experiments carried out in real cluttered indoor environments.

A solution to extract 3-D shapes from a structured light image is presented. The 2-D striped image is segmented into regions corresponding to quadratic surfaces. A fuzzy analysis of the stripe's properties allows one to locate discontinuities and to model stripe parts. These parts are tracked in consecutive images and matched in order to create regions. Segmentation results are presented which were obtained with a real scene consisting of multiple objects of arbitrary shapes.>