Francisco-Angel Moreno

Combination of UWB and GPS for indoor-outdoor vehicle localization

GPS receivers are satellite-based devices widely used for vehicle localization that, given their limitations, are not suitable for performing within indoor or dense urban environments. On the other hand ultra-wide band (UWB), a technology used for efficient wireless communication, has recently being used for vehicle localization in indoor environments with promising results. This paper focuses on the combination of both technologies for accurate positioning of vehicles in a mixed scenario (both indoor and outdoor situations), which is typical in some industrial applications. Our approach is based on combining sensor information in a Monte Carlo localization algorithm (also known as particle Filter), which has revealed its suitability for probabilistically coping with a variety of sensory data. The performance of our approach has been satisfactorily tested on a real robot, endowed with a UWB master antenna and a GPS receiver, within an indoor-outdoor scenario where three UWB slave antennas were placed in the indoor area.

An Instrumented Vehicle for Efficient and Accurate 3D Mapping of Roads

An electric vehicle equipped with a laser scanner and a highly accurate absolute positioning system aimed at surveying the geometry of roads is described in this article. The main advantages of the proposed system with respect to conventional topographic procedures are the possibility of achieving a much higher density of surveyed points and its efficiency while keeping almost the same accuracy—a standard deviation of 12 mm of absolute error. The data acquisition process is managed by an on-board computer which, in a synchronized way, deals with laser scanning and readings from three real-time-kinematics-enabled millimeter global positioning system (GPS) receivers. The three-dimensional position and orientation of the vehicle (6 degrees of freedom) all along its trajectory is calculated off-line by a custom software. The system also obtains the absolute coordinates of the road scanned points with that information that was obtained from the software. A rigorous description regarding the theory behind the 3D reconstruction and the calibration process is also presented in the article.

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.

ERODE: An efficient and robust outlier detector and its application to stereovisual odometry

This paper presents ERODE, an efficient outlier detector with a quality similar to that of standard RANSAC but at a fraction of its computational cost. In contrast to RANSAC-based methods which follow a hypothesis-and-verify approach, ERODE employs instead the whole set of observations together with a robust kernel to perform robustified least-squares minimization. Our proposal has important practical applications among computer vision problems, which we demonstrate with stereovisual odometry experiments with both simulated and real data.

A constant-time SLAM back-end in the continuum between global mapping and submapping

This work addresses the development and application of a novel approach, called sparser relative bundle adjustment (SRBA), which exploits the inherent flexibility of the relative bundle adjustment (RBA) framework to devise a continuum of strategies, ranging from RBA with linear graphs to classic bundle adjustment (BA) in global coordinates, where submapping with local maps emerges as a natural intermediate solution. This method leads to graphs that can be optimized in bounded time even at loop closures, regardless of the loop length. Furthermore, it is shown that the pattern in which relative coordinate variables are defined among keyframes has a significant impact on the graph optimization problem. By using the proposed scheme, optimization can be done more efficiently than in standard RBA, allowing the optimization of larger local maps for any given maximum computational cost. The main algorithms involved in the graph management, along with their complexity analyses, are presented to prove their bounded-time nature. One key advance of the present work is the demonstration that, under mild assumptions, the spanning trees for every single keyframe in the map can be incrementally built by a constant-time algorithm, even for arbitrary graph topologies. We validate our proposal within the scope of visual stereo simultaneous localization and mapping (SLAM) by developing a complete system that includes a front-end that seamlessly integrates several state-of-the-art computer vision techniques such as ORB features and bag-of-words, along with a decision scheme for keyframe insertion and a SRBA-based back-end that operates as graph optimizer. Finally, a set of experiments in both indoor and outdoor conditions is presented to test the capabilities of this approach. Open-source implementations of the SRBA back-end and the stereo front-end have been released online.

Experimental Validation of Depth Cameras for the Parameterization of Functional Balance of Patients in Clinical Tests

In clinical practice, patients’ balance can be assessed using standard scales. Two of the most validated clinical tests for measuring balance are the Timed Up and Go (TUG) test and the MultiDirectional Reach Test (MDRT). Nowadays, inertial sensors (IS) are employed for kinematic analysis of functional tests in the clinical setting, and have become an alternative to expensive, 3D optical motion capture systems. In daily clinical practice, however, IS-based setups are yet cumbersome and inconvenient to apply. Current depth cameras have the potential for such application, presenting many advantages as, for instance, being portable, low-cost and minimally-invasive. This paper aims at experimentally validating to what extent this technology can substitute IS for the parameterization and kinematic analysis of the TUG and the MDRT tests. Twenty healthy young adults were recruited as participants to perform five different balance tests while kinematic data from their movements were measured by both a depth camera and an inertial sensor placed on their trunk. The reliability of the camera’s measurements is examined through the Interclass Correlation Coefficient (ICC), whilst the Pearson Correlation Coefficient (r) is computed to evaluate the correlation between both sensor’s measurements, revealing excellent reliability and strong correlations in most cases.

Towards a Semantic Gas Source Localization Under Uncertainty

This work addresses the problem of efficiently and coherently locating a gas source in a domestic environment with a mobile robot, meaning efficiently the coverage of the shortest distance as possible and coherently the consideration of different gas sources explaining the gas presence. The main contribution is the exploitation, for the first time, of semantic relationships between the gases detected and the objects present in the environment to face this challenging issue. Our proposal also takes into account both the uncertainty inherent in the gas classification and object recognition processes. These uncertainties are combined through a probabilistic Bayesian framework to provide a priority-ordered list of (previously observed) objects to check. Moreover the proximity of the different candidates to the current robot location is also considered by a cost function, which output is used for planning the robot inspection path. We have conducted an initial demonstration of the suitability of our gas source localization approach by simulating this task within domestic environments for a variable number of objects, and comparing it with an greedy approach.