Vicente Arévalo

Shadow detection in colour high-resolution satellite images

Image shadow segmentation has become a major issue in satellite remote sensing because of the recent commercial availability of high‐resolution images. Detecting shadows is important for successfully carrying out applications such as change detection, land monitoring, object recognition, scene reconstruction, colour correction, etc. This paper presents a simple and effective procedure to segment shadow regions on high‐resolution colour satellite images. The method applies a region growing process on a specific band (namely, the c 3 component of the c 1 c 2 c 3 colour space). To gain in robustness and precision, the region expansion also imposes a restriction on the saturation and intensity values of the shadow pixels, as well as on their edge gradients. The proposed method has been successfully tested on QuickBird images acquired under different lighting conditions and covering both urban and rural areas.

Fast place recognition with plane-based maps

This paper presents a new method for recognizing places in indoor environments based on the extraction of planar regions from range data provided by a hand-held RGB-D sensor. We propose to build a plane-based map (PbMap) consisting of a set of 3D planar patches described by simple geometric features (normal vector, centroid, area, etc.). This world representation is organized as a graph where the nodes represent the planar patches and the edges connect planes that are close by. This map structure permits to efficiently select subgraphs representing the local neighborhood of observed planes, that will be compared with other subgraphs corresponding to local neighborhoods of planes acquired previously. To find a candidate match between two subgraphs we employ an interpretation tree that permits working with partially observed and missing planes. The candidates from the interpretation tree are further checked out by a rigid registration test, which also gives us the relative pose between the matched places. The experimental results indicate that the proposed approach is an efficient way to solve this problem, working satisfactorily even when there are substantial changes in the scene (lifelong maps).

Extrinsic calibration of a set of range cameras in 5 seconds without pattern

The integration of several range cameras in a mobile platform is useful for applications in mobile robotics and autonomous vehicles that require a large field of view. This situation is increasingly interesting with the advent of low cost range cameras like those developed by Primesense. Calibrating such combination of sensors for any geometric configuration is a problem that has been recently solved through visual odometry (VO) and SLAM. However, this kind of solution is laborious to apply, requiring robust SLAM or VO in controlled environments. In this paper we propose a new uncomplicated technique for extrinsic calibration of range cameras that relies on finding and matching planes. The method that we present serves to calibrate two or more range cameras in an arbitrary configuration, requiring only to observe one plane from different viewpoints. The conditions to solve the problem are studied, and several practical examples are presented covering different geometric configurations, including an omnidirectional RGB-D sensor composed of 8 range cameras. The quality of this calibration is evaluated with several experiments that demonstrate an improvement of accuracy over design parameters, while providing a versatile solution that is extremely fast and easy to apply.

Improving Piecewise Linear Registration of High-Resolution Satellite Images Through Mesh Optimization

Piecewise linear transformation is a powerful technique for coping with the registration of images affected by local geometric distortions, as it is usually the case of high-resolution satellite images. A key point when applying this technique is to divide the images to register according to a suitable common triangular mesh. This comprises two different aspects: where to place the mesh vertices (i.e., the mesh geometrical realization) and to set an appropriate topology upon these vertices (i.e., the mesh topological realization). This paper focuses on the latter and presents a novel method that improves the registration of two images by an iterative optimization process that modifies the mesh connectivity by swapping edges. For detecting if an edge needs to be swapped or not, we evaluate the registration improvement of that action on the two triangles connected by the edge. Another contribution of our proposal is the use of the mutual information for measuring the registration consistency within the optimization process, which provides more robustness to image changes than other well-known metrics such as normalized cross-correlation or sum of square differences. The proposed method has been successfully tested with different pairs of panchromatic QuickBird images (0.6 m/pixel of spatial resolution) of a variety of land covers (urban, residential, and rural) acquired under different lighting conditions and viewpoints.

SANCHO, a fair host robot. A description

This paper describes SANCHO, a mobile robot intended to perform within crowded areas as a servant, for instance as a fair or congress host. This robot has been constructed upon a commercial platform on which a number of sensors and devices have been integrated. A software control architecture has been implemented and adapted to this particular robot, enabling it to perform in human scenarios. Among the different subsystems of the control architecture developed for SANCHO, we highlight in this paper two of the most relevant ones: the navigation component which permits the robot to navigate in a safe and robust manner within crowded and dynamic environments, and the communication component which provides different possibilities for human-robot interaction. We illustrate the performance of SANCHO through a number of experiences carried out in public shows.

Applying image analysis and probabilistic techniques for counting olive trees in high-resolution satellite images

This paper proposes a method, that integrates image analysis and probabilistic techniques, for counting olive trees in high-resolution satellite images. Counting trees becomes significant for surveying and inventorying forests, and in certain cases relevant for assessing estimates of the production of plantations, as it is the case of the olive trees fields. The method presented in this paper exploits the particular characteristics of parcels, i.e. a certain reticular layout and a similar appearance of trees, to yield a probabilistic measure that captures the confident of each spot in the image to be an olive tree. Some promising experimental results have been obtained in satellite images taken from QuickBird.

Scene structure registration for localization and mapping

Image registration, and more generally scene registration, needs to be solved in mobile robotics for a number of tasks including localization, mapping, object recognition, visual odometry and loop-closure. This paper presents a flexible strategy to register scenes based on its planar structure, which can be used with different sensors that acquire 3D data like LIDAR, time-of-flight cameras, RGB-D sensors and stereo vision. The proposed strategy is based on the segmentation of the planar surfaces from the scene, and its representation using a graph which stores the geometric relationships between neighbouring planar patches. Uncertainty information from the planar patches is exploited in a hierarchical fashion to improve both the robustness and the efficiency of registration. Quick registration is achieved in indoor structured scenarios, offering advantages like a compact representation, and flexibility to adapt to different environments and sensors. Our method is validated with different sensors: a hand-held RGB-D camera and an omnidirectional RGB-D sensor; and for different applications: from visual-range odometry to loop closure and SLAM. A geometric representation based on planar patches is proposed for scene registration.Photometric information is combined with the geometric description for robustness.A novel formulation encoding uncertainty information is proposed to improve accuracy.An implementation of this work is freely available (MRPT and PCL).

Extrinsic calibration of a set of 2D laser rangefinders

The integration of several 2D laser rangefinders in a vehicle is a common resource employed for 3D mapping, obstacle detection and navigation. The extrinsic calibration between such sensors (i.e. finding their relative poses) is required to exploit effectively the sensor measurements and to perform data fusion. The approaches found in the literature to obtain such calibration either rely on the approximated parameters from the rig construction or propose ad-hoc solutions for specific LRF rigs. In this paper we present a novel solution for the extrinsic calibration of a set of at least three laser scanners from the information provided by the sensor measurements. This method only requires the lasers to observe a common planar surface from different orientations, thus there is no need of any specific calibration pattern. This calibration technique can be used with almost any geometric sensor configuration (except for sensors scanning parallel planes), and constitutes a versatile solution that is accurate, fast and easy to apply. This approach is validated with both simulated and real data.

A compact planar-patch descriptor based on color

The representation of the world upon planar patches has proven to be simple, robust and useful for a variety of robotic tasks, including SLAM, autonomous navigation, or scene recognition. In this work we investigate how to incorporate color information into such representation to improve the matching of planar patches while maintaining the model compactness, which is essential for real-time applications. We propose a descriptor based on the dominant color of the patch, which is defined as the center of the biggest cluster in the patch histogram. In the paper, different color spaces and methods for extracting the dominant color are analyzed. We compare this descriptor with a recent proposal (saturated hue based histogram) and provide some conclusions on the trade-off between their descriptiveness and compactness. Finally, we present experimental results showing how our color descriptor can be exploited to increase the efficiency of both: plane-based place recognition and planar patch categorization.

An Automated Surveying and Marking System for Continuous Setting-out of Tunnels

Setting-out is a crucial and frequently repeated process in construction and civil engineering. It is carried out by qualified operators who, making use of surveying stations, identify reference points to guide workers in their tasks. In this paper, we focus on the particular case of setting-out road/train tunnels and propose a system that automatically performs the setting-out operation of the tunnel section to be perforated. The presented system, called Tunnel Continuous Setout TCS, integrates a scanning device that surveys the excavation front and a laser projector that continuously displays the actual tunnel section computed as the intersection between the surveyed terrain and the planned tunnel section. Thus, the topographer intervention is only required for the precise positioning of the TCS device at the beginning of each working stretch, which depends on the operational range of its components limited to 25 meters in the current implementation. A prototype of the TCS system has been employed in a real construction site proving its benefits and advantages with respect to the traditional setting-out techniques.