Laser range finder and omnidirectional cameras are becoming a promising combination of sensors to extract rich environmental information. This information includes textured plane extraction, vanishing points, catadioptric projection of vertical and horizontal lines, or invariant image features. However, many indoor scenes do not have enough texture information to describe the environment. In these situations, vertical edges could be used instead. This study presents a sensor model that is able to extract three‐dimensional position of vertical edges from a range‐augmented omnidirectional vision sensor. Using the unified spherical model for central catadioptric sensors and the proposed sensor model, the vertical edges are locally projected, improving the data association for mapping and localisation. The proposed sensor model was tested using the FastSLAM algorithm to solve the simultaneous localisation and mapping problem in indoor environments. Real‐world qualitative and quantitative experiments are presented to validate the proposed approach using a Pioneer‐3DX mobile robot equipped with a URG‐04LX laser range finder and an omnidirectional camera with parabolic mirror.

This paper presents a method to build large-scale mosaics adapted to underwater sonar imagery. By assuming a simplified imaging model, we propose to address the registrations between images using Fourier-based methods which, unlike feature-based methods, prove well suited to handle the characteristics of forward-looking sonar images, such as low resolution, noise, occlusions and moving shadows. The registration between spatially and temporally distant images resulting from loop-closing situations or registrations in featureless areas are feasible, overcoming the main difficulties of feature-based methods. The problem is cast as a pose-based graph optimization, taking into account the uncertainties of the pairwise registrations and being able to incorporate navigation information. After the optimization, a consistent mosaic from different tracklines is generated with increased resolution and higher signal-to-noise ratio than the original images, while the vehicle motion in x,y and heading is also estimated.

Robot map navigation and localization are challenging tasks that require the solving of the data association problem for local and global features. Data fusion allows the advantages of two or more sensors to be combined, and complementary cooperation can be obtained. This paper presents two methods to embed depth information in omnidirectional images using the extrinsic calibration of a 2D laser range finder and a central catadioptric camera. The methods presented do not require a visible laser beam, but they assume the planar checkerboard patterns are visible for both the catadioptric camera and the 2D laser range finder. Unlike other approaches, the methods proposed used an invisible laser trace, and they are evaluated at pixel error level using ground truth data from the calibration patterns projected in the omnidirectional image. Results include a mean square error analysis of all calibration poses, and laser point projection on indoor omnidirectional images. We think that embedding range information in omnidirectional images is an interesting tool for data fusion approaches, which can be used in robot map building and localization.

In this paper a colour texture segmentation method, which unifies region and boundary information, is proposed. The algorithm uses a coarse detection of the perceptual (colour and texture) edges of the image to adequately place and initialise a set of active regions. Colour texture of regions is modelled by the conjunction of non-parametric techniques of kernel density estimation (which allow to estimate the colour behaviour) and classical co-occurrence matrix based texture features. Therefore, region information is defined and accurate boundary information can be extracted to guide the segmentation process. Regions concurrently compete for the image pixels in order to segment the whole image taking both information sources into account. Furthermore, experimental results are shown which prove the performance of the proposed method.

This paper describes the improvements achieved in our mosaicking system to assist unmanned underwater vehicle navigation. A major advance has been attained in the processing of images of the ocean floor when light absorption effects are evident. Due to the absorption of natural light, underwater vehicles often require artificial light sources attached to them to provide the adequate illumination for processing underwater images. Unfortunately, these flashlights tend to illuminate the scene in a nonuniform fashion. In this paper a technique to correct non-uniform lighting is proposed. The acquired frames are compensated through a point-by-point division of the image by an estimation of the illumination field. Then, the gray-levels of the obtained image remapped to enhance image contrast. Experiments with real images are presented.

Addresses the problem of estimating the motion of an autonomous underwater vehicle (AUV), while it constructs a visual map ("mosaic" image) of the ocean floor. The vehicle is equipped with a down-looking camera which is used to compute its motion with respect to the seafloor. As the mosaic increases in size, a systematic bias is introduced in the alignment of the images which form the mosaic. Therefore, this accumulative error produces a drift in the estimation of the position of the vehicle. When the arbitrary trajectory of the AUV crosses over itself, it is possible to reduce this propagation of image alignment errors within the mosaic. A Kalman filter with augmented state is proposed to optimally estimate both the visual map and the vehicle position.

This paper presents an automatic vision-based system for UUV station keeping. The vehicle is equipped with a down-looking camera, which provides images of the sea-floor. The station keeping system is based on a feature-based motion detection algorithm, which exploits standard correlation and explicit textural analysis to solve the correspondence problem. A visual map of the area surveyed by the vehicle is constructed to increase the flexibility of the system, allowing the vehicle to position itself when it has lost the reference image. The testing platform is the URIS underwater vehicle. Experimental results demonstrating the behavior of the system on a real environment are presented.

This paper presents an approach to ameliorate the reliability of the correspondence points relating two consecutive images of a sequence. The images are especially difficult to handle, since they have been acquired by a camera looking at the sea floor while carried by an underwater robot. Underwater images are usually difficult to process due to light absorption, changing image radiance and lack of well-defined features. A new approach based on gray-level region matching and selective texture analysis significantly improves the matching reliability.

Mosaics have been commonly used as visual maps for undersea exploration and navigation. The position and orientation of an underwater vehicle can be calculated by integrating the apparent motion of the images which form the mosaic. A feature-based mosaicking method is proposed in this paper. The creation of the mosaic is accomplished in four stages: feature selection and matching, detection of points describing the dominant motion, homography computation and mosaic construction. In this work we demonstrate that the use of color and textures as discriminative properties of the image can improve, to a large extent, the accuracy of the constructed mosaic. The system is able to provide 3D metric information concerning the vehicle motion using the knowledge of the intrinsic parameters of the camera while integrating the measurements of an ultrasonic sensor. The experimental results of real images have been tested on the GARBI underwater vehicle.

A new method for the automated selection of colour features is described. The algorithm consists of two stages of processing. In the first, a complete set of colour features is calculated for every object of interest in an image. In the second stage, each object is mapped into several n-dimensional feature spaces in order to select the feature set with the smallest variables able to discriminate the remaining objects. The evaluation of the discrimination power for each concrete subset of features is performed by means of decision trees composed of linear discrimination functions. This method can provide valuable help in outdoor scene analysis where no colour space has been demonstrated as being the most suitable. Experiment results recognizing objects in outdoor scenes are reported.

One of the major problems in machine vision is the segmentation of images of natural scenes. This paper presents a new proposal for the image segmentation problem which has been based on the integration of edge and region information. The main contours of the scene are detected and used to guide the posterior region growing process. The algorithm places a number of seeds at both sides of a contour allowing stating a set of concurrent growing processes. A previous analysis of the seeds permits to adjust the homogeneity criterion to the regions's characteristics. A new homogeneity criterion based on clustering analysis and convex hull construction is proposed.

Image segmentation of natural scenes constitutes a major problem in machine vision. This paper presents a new proposal for the image segmentation problem which has been based on the integration of edge and region information. This approach begins by detecting the main contours of the scene which are later used to guide a concurrent set of growing processes. A previous analysis of the seed pixels permits adjustment of the homogeneity criterion to the region's characteristics during the growing process. Since the high variability of regions representing outdoor scenes makes the classical homogeneity criteria useless, a new homogeneity criterion based on clustering analysis and convex hull construction is proposed. Experimental results have proven the reliability of the proposed approach.

This paper presents an interface for the interaction between a human and an adapted kitchen where different elements, including a robot, have to be controlled. After the global structure of the interface is described a more detailed explanation is presented on the way the interface offers the user an easy and friendly way to select the desired objects and to provide their position to the robot for their manipulation.

This paper describes a method to achieve the most relevant contours of an image. The presented method proposes to integrate the information of the local contours from chromatic components such as H, S and I, taking into account the criteria of coherence of the local contour orientation values obtained from each of these components. The process is based on parametrizing pixel by pixel the local contours (magnitude and orientation values) from the H, S and I images. This process is carried out individually for each chromatic component. If the criterion of dispersion of the obtained orientation values is high, this chromatic component will lose relevance. A final processing integrates the extracted contours of the three chromatic components, generating the so-called integrated contours image.