This paper proposes an incremental scheme for visual landmark learning and recognition. The feature selection stage characterises the landmark using the Opponent SIFT, a color-based variant of the SIFT descriptor. To reduce the dimensionality of this descriptor, an incremental non-parametric discriminant analysis is conducted to seek directions for efficient discrimination (incremental eigenspace learning). On the other hand, the classification stage uses the incremental envolving clustering method (ECM) to group feature vectors into a set of clusters (incremental prototype learning). Then, the final classification is conducted based on the k-nearest neighbor approach, whose prototypes were updated by the ECM. This global scheme enables a classifier to learn incrementally, on-line, and in one pass. Besides, the ECM allows to reduce the memory and computation expenses. Experimental results show that the proposed recognition system is well suited to be used by an autonomous mobile robot.

Hybrid maps combine metric and topological information for efficiently managing large-scale environments. In a feature-based mapping framework, this paper describes the application of a spectral clustering approach for automatically detecting the transitions between subsequently traversed local maps. Contrary to recently proposed approaches, this algorithm considers each individual map feature as a node of a graph whose edges link two nodes if they are simultaneously observed. Thus, given a sequence of observations, an auxiliary graph is incrementally built whose edges carry non-negative weights according to the locality of the features. Given a feature, its locality defines the set of features that has been observed simultaneously with it at least once. At each execution of the mapping approach, the feature-based graph is split into two subgraphs using a normalized spectral clustering algorithm. If the graph partition is validated, the algorithm determines that the robot is moving into a new area and a new local map is generated. We have tested the proposed approach in real environments where features are obtained using 2D laser sensors or vision. Experimental results demonstrate the performance of the proposal.

In this paper, we describe a new approach which uses scale-invariant image features to estimate the motion of a stereo head. These point features are matched between pairs of frames and linked into image trajectories at video rate, generating what it is called visual odometry, i.e. motion estimates from visual input alone. With respect to previously proposed approaches, the main novelty of our proposal is that the matching between sets of features associated to stereo pairs and between sets of image features associated to consecutive frames are conducted by means of a fast combined constraint matching algorithm. Besides, the efficiency of the approach is increased by using a closed-form solution to estimate the final robot displacement between consecutive acquired frames. We have tested the proposed approach for navigational purposes in a real environment. Experimental results demonstrate the performance of the proposal.

In this paper, an approach for fitting a circle to 2-D data which represent only a small part of the curve is described. This approach deals with the particular case where data is specified in terms of its Cartesian coordinates and the errors in both coordinates are not independent. Besides, an associated uncertainty ellipse which describes the measurement error and the variance matrix associated to the estimated parameters are obtained. This method is particularly well designed to fit a circle to a set of measured range readings provided by a 2-D laser range finder when these range readings are specified in terms of its Cartesian coordinates. Therefore, it has been successfully applied to acquire circle-shaped landmarks in a mobile robotics navigation task.

This paper presents a geometrical feature detection system to use with conventional 2D laser rangefinders. This system consists of three main modules: data acquisition and pre-processing, rupture and breakpoint detection and feature extraction. The novelty of this system is a new efficient approach for natural feature extraction based on curvature estimation. This approach permits to extract and characterise line segments, corners and curve segments from the laser scan. Experimental results show that the proposed approach is very fast and permit to verify its effectiveness in indoor and outdoor environments

This paper presents a hybrid layered architecture for intelligent transportation. The system relies on integrating odometry, DGPS and compass readings for localization and deliberative planning, whereas the path is tracked at low level by means of a CBR based reactive navigation algorithm capable of adapting to the vehicle kinematics and environment conditions through learning. The system has been successfully tested using a 4-wheeled outdoors robot Pioneer AT

This paper presents a visual attention mechanism for the acquisition of landmarks in an arbitrary scene. The proposed mechanism consists of two consecutive selection stages. The first one employs classical preattentive saliency computa tions to select a reduced set of interest regions from the whole input image (data-driven stage). The second stage selects from the output of the first selection stage the region that can be considered as a potential landmark (model-driven stage). This potential landmark is the input of the attentive stage, that must characterize it and finally determine if this object is a real landmark. The used imaging sensor is a stereo vision system which is capable of providing depth data as well as color images. This stereo vision system is mounted on an autonomous mobile robot and serves map-building and localisation purposes. We present results achieved by applying the proposed visual attention scheme to on-line acquired stereo pairs of indoor and outdoor scenes.