Eduardo Fernandez-Moral

Bio: Eduardo Fernandez-Moral is an academic researcher from French Institute for Research in Computer Science and Automation. The author has contributed to research in topics: Visual odometry & Image segmentation. The author has an hindex of 8, co-authored 16 publications receiving 300 citations. Previous affiliations of Eduardo Fernandez-Moral include University of Málaga.

Fast place recognition with plane-based maps

Abstract: 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 2d laser-rangefinder and a camera based on scene corners

Abstract: Robots are often equipped with 2D laser-rangefinders (LRFs) and cameras since they complement well to each other. In order to correctly combine measurements from both sensors, it is required to know their relative pose, that is, to solve their extrinsic calibration. In this paper we present a new approach to such problem which relies on the observations of orthogonal trihedrons which are profusely found as corners in human-made scenarios. Thus, the method does not require any specific pattern, which turns the calibration process fast and simpler to perform. The estimated relative pose has proven to be also very precise since it uses two different types of constraints, line-to-plane and point-to-plane, as a result of a richer configuration than previous proposals that relies on plane or V-shaped patterns. Our approach is validated with synthetic and real experiments, showing better performance than the state-of-art methods.

A New Metric for Evaluating Semantic Segmentation: Leveraging Global and Contour Accuracy

Abstract: Semantic segmentation of images is an important issue for intelligent vehicles and mobile robotics because it offers basic information which can be used for complex reasoning and safe navigation. Different solutions have been proposed for this problem along the last two decades, where recent deep neural networks approaches have shown very promising results in the context of urban navigation. One of the main problems when comparing different semantic segmentation solutions is how to select an appropriate metric to evaluate their accuracy. On the one hand, classic metrics do not measure properly the accuracy on the object contours, which is important in urban driving to differentiate road from sidewalk for instance. On the other hand, contour-based metrics [1] disregard the information far from class contours. This paper explores the problem multi-modal image segmentation, and presents a new metric to leverage global and contour accuracy in a simple formulation. This metric is validated with the evaluation of several semantic segmentation solutions that exploit RGB-D images to rank these solutions taking into account the quality of the segmented contours. We also present a comparative analysis of several commonly used metrics together with a statistical analysis of their correlation.

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

Abstract: 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 differ- ent 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 demon- strate an improvement of accuracy over design parameters, while providing a versatile solution that is extremely fast and easy to apply.

SeqSLAM : visual route-based navigation for sunny summer days and stormy winter nights

Abstract: Learning and then recognizing a route, whether travelled during the day or at night, in clear or inclement weather, and in summer or winter is a challenging task for state of the art algorithms in computer vision and robotics. In this paper, we present a new approach to visual navigation under changing conditions dubbed SeqSLAM. Instead of calculating the single location most likely given a current image, our approach calculates the best candidate matching location within every local navigation sequence. Localization is then achieved by recognizing coherent sequences of these “local best matches”. This approach removes the need for global matching performance by the vision front-end - instead it must only pick the best match within any short sequence of images. The approach is applicable over environment changes that render traditional feature-based techniques ineffective. Using two car-mounted camera datasets we demonstrate the effectiveness of the algorithm and compare it to one of the most successful feature-based SLAM algorithms, FAB-MAP. The perceptual change in the datasets is extreme; repeated traverses through environments during the day and then in the middle of the night, at times separated by months or years and in opposite seasons, and in clear weather and extremely heavy rain. While the feature-based method fails, the sequence-based algorithm is able to match trajectory segments at 100% precision with recall rates of up to 60%.

SegMatch: Segment based place recognition in 3D point clouds

Abstract: Place recognition in 3D data is a challenging task that has been commonly approached by adapting image-based solutions. Methods based on local features suffer from ambiguity and from robustness to environment changes while methods based on global features are viewpoint dependent. We propose SegMatch, a reliable place recognition algorithm based on the matching of 3D segments. Segments provide a good compromise between local and global descriptions, incorporating their strengths while reducing their individual drawbacks. SegMatch does not rely on assumptions of ‘perfect segmentation’, or on the existence of ‘objects’ in the environment, which allows for reliable execution on large scale, unstructured environments. We quantitatively demonstrate that SegMatch can achieve accurate localization at a frequency of 1Hz on the largest sequence of the KITTI odometry dataset. We furthermore show how this algorithm can reliably detect and close loops in real-time, during online operation. In addition, the source code for the SegMatch algorithm is made publicly available1.

Fast plane extraction in organized point clouds using agglomerative hierarchical clustering

Abstract: Real-time plane extraction in 3D point clouds is crucial to many robotics applications. We present a novel algorithm for reliably detecting multiple planes in real time in organized point clouds obtained from devices such as Kinect sensors. By uniformly dividing such a point cloud into nonoverlapping groups of points in the image space, we first construct a graph whose node and edge represent a group of points and their neighborhood respectively. We then perform an agglomerative hierarchical clustering on this graph to systematically merge nodes belonging to the same plane until the plane fitting mean squared error exceeds a threshold. Finally we refine the extracted planes using pixel-wise region growing. Our experiments demonstrate that the proposed algorithm can reliably detect all major planes in the scene at a frame rate of more than 35Hz for 640×480 point clouds, which to the best of our knowledge is much faster than state-of-the-art algorithms.