R. Vazquez-Martin

Natural landmark extraction for mobile robot navigation based on an adaptive curvature estimation

This paper proposes a geometrical feature detection system which is to be used with conventional 2D laser range finders. It consists of three main modules: data acquisition and pre-processing, segmentation and landmark extraction and characterisation. The novelty of this system is a new approach for laser data segmentation based on an adaptive curvature estimation. Contrary to other works, this approach divides the laser scan into line and curve segments. Then, these items are used to directly extract several types of landmarks associated with real and virtual features of the environment (corners, center of tree-like objects, line segments and edges). For each landmark, characterisation provides not only the parameter vector, but also complete statistical information, suitable to be used in a localization and mapping algorithm. Experimental results show that the proposed approach is efficient to detect landmarks for structured and semi-structured environments.

Feature extraction from laser scan data based on curvature estimation for mobile robotics

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

Curvature-Based Environment Description for Robot Navigation Using Laser Range Sensors

This work proposes a new feature detection and description approach for mobile robot navigation using 2D laser range sensors. The whole process consists of two main modules: a sensor data segmentation module and a feature detection and characterization module. The segmentation module is divided in two consecutive stages: First, the segmentation stage divides the laser scan into clusters of consecutive range readings using a distance-based criterion. Then, the second stage estimates the curvature function associated to each cluster and uses it to split it into a set of straight-line and curve segments. The curvature is calculated using a triangle-area representation where, contrary to previous approaches, the triangle side lengths at each range reading are adapted to the local variations of the laser scan, removing noise without missing relevant points. This representation remains unchanged in translation or rotation, and it is also robust against noise. Thus, it is able to provide the same segmentation results although the scene will be perceived from different viewpoints. Therefore, segmentation results are used to characterize the environment using line and curve segments, real and virtual corners and edges. Real scan data collected from different environments by using different platforms are used in the experiments in order to evaluate the proposed environment description algorithm.

Fast laser scan matching approach based on adaptive curvature estimation for mobile robots

This paper describes a complete laser-based approach for tracking the pose of a robot in a dynamic environment. The main novelty of this approach is that the matching between consecutively acquired scans is achieved using their associated curvature-based representations. The proposed scan matching algorithm consists of three stages. Firstly, the whole raw laser data is segmented into groups of consecutive range readings using a distance-based criterion and the curvature function for each group is computed. Then, this set of curvature functions is matched to the set of curvature functions associated to the previously acquired laser scan. Finally, characteristic points of pairwise curvature functions are matched and used to correctly obtain the best local alignment between consecutive scans. A closed form solution is employed for computing the optimal transformation and minimizing the robot pose shift error without iterations. Thus, the system is outstanding in terms of accuracy and computation time. The implemented algorithm is evaluated and compared to three state of the art scan matching approaches.

A novel approach for salient image regions detection and description

This paper proposes a new algorithm for visual landmarks detection and description. The detection is achieved using a hierarchical grouping mechanism, which combines a color contrast measure defined between regions with internal region descriptors and with attributes of the shared boundary. This detector reliably finds the same salient regions under different viewing conditions. Then, geometrically and photometrically normalized regions are characterized by a kernel-based descriptor. This descriptor is rotation-invariant and robust against noise. Several tests are conducted in order to compare the proposed approach with other similar approaches. Experimental results prove that the performance of our proposal is high in terms of computational consuming and visual landmark detection and description abilities.

A Hough-based method for concurrent mapping and localization in indoor environments

This paper proposes a method to solve the simultaneous localization and map building problem based on segments extracted from local sonar based occupation maps. These segments are categorized as new obstacle boundaries of a simultaneously built global segment-based map or as prolongations of previously extracted boundaries. The method is adequate for indoor office-like environments, specially for those environments that can be suitable modelled by a set of segments. The method has been proved in several experiments in the same indoor environment with successful results.

Spatio-temporal feature-based keyframe detection from video shots using spectral clustering

Keyframe detection is a fundamental component in approaches for large-scale mapping and scene recognition. Assuming that the detection is applied to a set of continuously captured frames, this paper presents a keyframe detector that not only considers the frame content to quantify appearance changes on the sequence, but also the temporal accumulation of evidence. If frames are described as a set of local features, our algorithm proposes a unified framework for comparing local features acquired from consecutive frames by the building of an auxiliary graph-based on the locality of features. Spectral clustering is then employed to obtain tentative graph partitions. Validated partitions will be associated to keyframes. It should be noted that the approach does not need to estimate the motion of the camera, and that the similarity measure defined within this framework can be used for any sort of feature. Experimental results using different types of visual features show the strength of our representation. Moreover, an evaluation methodology has been defined for the quantitative comparison of our keyframe detector against other similar approaches.

LESS-mapping: Online environment segmentation based on spectral mapping

Given the features obtained from a sequence of consecutively acquired sensor readings, this paper proposes an on-line algorithm for unsupervisedly detecting a transition on this sequence, i.e. the frame that divides the sequence into two tightly related parts that are dissimilar between them. Contrary to recently proposed approaches that address this partitioning problem dealing with a sequence of robots poses, our proposal considers each individual feature as a node of an incrementally built graph whose edges link two nodes if their associated features were simultaneously observed. These graph 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 algorithm, the feature-based graph is split into two subgraphs using a normalized spectral clustering algorithm. The obtained partitions correspond to those parts in the environment that share the minimum amount of information. If this graph partition is validated, the algorithm determines that there is a significant change on the perceived scenario, assuming that a transition area has been traversed. In a map partitioning framework, we have tested the proposed approach in real environments where features are obtained using 2D laser sensors or vision (stereo and monocular cameras). The statistical evaluation of the experimental results demonstrates the performance of the proposal.

An Algorithm for Fitting 2-D Data on the Circle: Applications to Mobile Robotics

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.

Data-and Model-driven Attention Mechanism for Autonomous Visual Landmark Acquisition

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.