A key component of a mobile robot system is the ability to localize itself accurately and, simultaneously, to build a map of the environment. Most of the existing algorithms are based on laser range finders, sonar sensors or artificial landmarks. In this paper, we describe a vision-based mobile robot localization and mapping algorithm, which uses scale-invariant image features as natural landmarks in unmodified environments. The invariance of these features to image translation, scaling and rotation makes them suitable landmarks for mobile robot localization and map building. With our Triclops stereo vision system, these landmarks are localized and robot ego-motion is estimated by least-squares minimization of the matched landmarks. Feature viewpoint variation and occlusion are taken into account by maintaining a view direction for each landmark. Experiments show that these visual landmarks are robustly matched, robot pose is estimated and a consistent three-dimensional map is built. As image features are not noise-free, we carry out error analysis for the landmark positions and the robot pose. We use Kalman filters to track these landmarks in a dynamic environment, resulting in a database map with landmark positional uncertainty.

We have previously developed a mobile robot system which uses scale invariant visual landmarks to localize and simultaneously build a 3D map of the environment In this paper, we look at global localization, also known as the kidnapped robot problem, where the robot localizes itself globally, without any prior location estimate. This is achieved by matching distinctive landmarks in the current frame to a database map. A Hough Transform approach and a RANSAC approach for global localization are compared, showing that RANSAC is much more cjficicnt. Moreover, robust global localization can be achieved by matching a small sub-map of the local region built from multiple frames.

This paper provides a new solution to the simultaneous localization and mapping (SLAM) problem with six degrees of freedom. A fast variant of the Iterative Closest Points (ICP) algorithm registers 3D scans taken by a mobile robot into a common coordinate system and thus provides relocalization. Hereby, data association is reduced to the problem of searching for closest points. Approximation algorithms for this searching, namely, approximate kd-trees and box decomposition trees, are presented and evaluated in this paper. A solution to 6D SLAM that considers all free parameters in the robot pose is built based on 3D scan matching.

Abstract. 6D SLAM (Simultaneous Localization and Mapping) or 6D Concurrent Localization and Mapping of mobile robots considers six dimensions for the robot pose, namely, the x, y and z coordinates and the roll, yaw and pitch angles. Robot motion and localization on natural surfaces, e.g., driving with a mobile robot outdoor, must regard these degrees of freedom. This paper presents a robotic mapping method based on locally consistent 3D laser range scans. Scan matching, combined with a heuristic for closed loop detection and a global relaxation method, results in a highly precise mapping system for outdoor environments. The mobile robot Kurt3D was used to acquire data of the Schloss Birlinghoven campus. The resulting 3D map is compared with ground truth, given by an aerial photograph. 1

We propose a considerably faster approximation of the well known SIFT method. The main idea is to use efficient data structures for both, the detector and the descriptor. The detection of interest regions is considerably speed-up by using an integral image for scale space computation. The descriptor which is based on orientation histograms, is accelerated by the use of an integral orientation histogram. We present an analysis of the computational costs comparing both parts of our approach to the conventional method. Extensive experiments show a speed-up by a factor of eight while the matching and repeatability performance is decreased only slightly.

This paper presents an approach to build high resolution digital elevation maps from a sequence of unregistered low altitude stereovision image pairs. The approach first uses a visual motion estimation algorithm that determines the 3D motions of the cameras between consecutive acquisitions, on the basis of visually detected and matched environment features. An extended Kalman filter then estimates both the 6 position parameters and the 3D positions of the memorized features as images are acquired. Details are given on the filter implementation and on the estimation of the uncertainties on the feature observations and motion estimations. Experimental results show that the precision of the method enables to build spatially consistent very large maps. 1.

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The aim of this article is to present the potential of Kernel Principal Component Analysis (Kernel PCA) in the field of vision based robot localization. Using Kernel PCA we can extract features from the visual scene of a mobile robot. The analysis is applied only to local features so as to guarantee better computational performance as well as translation invariance. Compared with the classical Principal Component Analysis (PCA), Kernel PCA results show superiority in localization and robustness in presence of noisy scenes. The key success of the kernel PCA is the use of fractional power polynomial kernels.

José is a visually guided autonomous robotic waiter. He circulates around a room populated by groups of people, politely serving appetizers to humans. The serving task combines elements of robotics with human computer interaction, challenging control architecture with multiple task integration. This paper describes our purely vision-based approach to this task. Methods for mapping, localization and navigation are presented and discussed, including issues of safety for both robots and humans. Our work on human-robot interaction is covered, as well as our solutions to various tasks specific to serving food. We present results of our methods from sample experiments in our laboratory. We further discuss our experiences at the 2001 AAAI mobile robot “Hors D’œuvres Anyone? ” competition, at which José took first prize. 1

Abstract. We present a new metric between histograms such as SIFT descriptors and a linear time algorithm for its computation. It is common practice to use the L2 metric for comparing SIFT descriptors. This practice assumes that SIFT bins are aligned, an assumption which is often not correct due to quantization, distortion, occlusion etc. In this paper we present a new Earth Mover’s Distance (EMD) variant. We show that it is a metric (unlike the original EMD [1] which is a metric only for normalized histograms). Moreover, it is a natural extension of the L1 metric. Second, we propose a linear time algorithm for the computation of the EMD variant, with a robust ground distance for oriented gradients. Finally, extensive experimental results on the Mikolajczyk and Schmid dataset [2] show that our method outperforms state of the art distances. 1