The localization capability is central to basic navigation tasks and motivates development of various visual navigation systems. These systems can be used both as navigational aids for visually impaired or in the context of autonomous mobile systems. In this paper we describe a two stage approach for localization in indoor environments. In the first stage, the environment is partitioned into several locations, each characterized by a set of scale-invariant keypoints and their associated descriptors. In the second stage the keypoints of the query view are integrated probabilistically yielding an estimate of most likely location. The emphasis of our approach in the environment model acquisition stage is on the selection of discriminative features, best suited for characterizing individual locations. The high recognition rate is maintained with only 10 % of the originally detected features, yielding a substantial speedup in recognition. The ambiguities due to the self-similarity and dynamic changes in the environment are resolved by exploiting spatial relationships between locations captured by Hidden Markov Model. Once the most likely location is determined, the relative pose of the camera with respect to the reference view can be computed. 1

In this paper, we present a new approach for omnidirectional vision-based self-localization in the RoboCup Middle-Size League. The omnidirectional vision sensor is used as a range finder (like a laser or a sonar) sensitive to colors transitions instead of nearest obstacles. This makes it possible to have a more reach information about the environment, because it is possible to discriminate between di#erent objects painted in di#erent colors. We implemented a Monte-Carlo localization system slightly adapted to this new type of range sensor. The system runs in real time on a low-cost pc. Experiments demonstrated the robustness of the approach. Event if the system was implemented and tested in the RoboCup Middle-Size field, the system could be used in other environments.

Abstract. The localization capability of a mobile robot is central to basic navigation and map building tasks. We describe a probabilistic environment model which facilitates global localization scheme by means of location recognition. In the exploration stage the environment is partitioned into several locations, each characterized by a set of scale-invariant keypoints. The descriptors associated with these keypoints can be robustly matched despite the changes in contrast, scale and affine distortions. We demonstrate the efficacy of these features for location recognition, where given a new view the most likely location from which this view came is determined. The misclassifications due to dynamic changes in the environment or inherent location appearance ambiguities are overcome by exploiting the location neighborhood relationships captured by a Hidden Markov Model. We report the recognition performance of this approach in an indoor environment consisting of eighteen locations and discuss the suitability of this approach for a more general class of recognition

We present a simple approach for vision-based path following for a mobile robot. Based upon a novel concept called the funnel lane, the coordinates of feature points during the replay phase are compared with those obtained during the teaching phase in order to determine the turning direction. Increased robustness is achieved by coupling the feature coordinates with odometry information. The system requires a single off-the-shelf, forward-looking camera with no calibration (either external or internal, including lens distortion). Implicit calibration of the system is needed only in the form of a single controller gain. The algorithm is qualitative in nature, requiring no map of the environment, no image Jacobian, no homography, no fundamental matrix, and no assumption about a flat ground plane. Experimental results demonstrate the capability of real-time autonomous navigation in both indoor and outdoor environments, on flat, slanted, and rough terrain with dynamic occluding objects for distances of hundreds of meters. We also demonstrate that the same approach works with wide-angle and omnidirectional cameras with only slight modification.

Summary. In this paper an existing method for indoor Simultaneous Localisation and Mapping (SLAM) is extended to operate in large outdoor environments using an omnidirectional camera as its principal external sensor. The method, RatSLAM, is based upon computational models of the area in the rat brain that maintains the rodent’s idea of its position in the world. The system uses the visual appearance of different locations to build hybrid spatial-topological maps of places it has experienced that facilitate relocalisation and path planning. A large dataset was acquired from a dynamic campus environment and used to verify the system’s ability to construct representations of the world and simultaneously use these representations to maintain localisation.

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One of the most challenging issue in mobile robot navigation is the localization problem in densely populated environments. In this paper, we present a new approach for vision-based localization able to solve this problem. The omnidirectional camera is used as a range finder sensitive to the distance of color transitions, whereas classical range finders, like lasers or sonars, are sensitive to the distance of the nearest obstacles. The well-known Monte-Carlo localization technique was adapted for this new type of range sensor. The system runs in real time on a low-cost pc. In this paper we present experiments, performed in a crowded RoboCup Middle-size field, proving the robustness of the approach to the occlusions of the vision sensor by moving obstacles (e.g other robots); occlusions that are very likely to occur in a real environment. Although, the system was implemented for the RoboCup environment, the system can be used in more general environments.

The scale invariant feature transform and the integral invariants are two well known approaches for visual feature extraction. Each of these approaches has been successfully applied to global localization of mobile robots. In this paper, we propose applying a combination of the two concepts. We demonstrate that extracting the integral invariants from the scale space does indeed improve the localization accuracy. We also show that the computation time of the proposed approach is much less than the scale invariant feature transform. 1

Location-based based augmented