David Schleicher

University of Alcalá, Alcalá de Henares, Madrid, Spain

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Publications (16)3.94 Total impact

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    ABSTRACT: This paper presents a new real-time hierarchical (topological/metric) localization system applied to the robust self-location of a vehicle in large-scale urban environments. Our proposal improves the current vehicle navigation systems based only on GPS sensor. It is exclusively based on the information provided by both, a low-cost wide-angle stereo camera and a low-cost GPS. A low level metric process obtains a 3D sequential mapping of natural landmarks and the vehicle location/orientation. GPS measurements are integrated within this low level, improving vehicle positioning. A higher topological processing level, based on fingerprints and the multi level relaxation (MLR) algorithm, has been added to reduce the global error keeping real-time constraints. Some experimental tests, using a real car navigation system on urban environments with loop closures, have been carried out. Main results and conclusions are presented.
    Signal Processing. 01/2010;
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    ABSTRACT: In this paper we present a new real-time hierarchical (topological/metric) Visual SLAM system focusing on the localization of a vehicle in large-scale outdoor urban environments. It is exclusively based on the visual information provided by a cheap wide-angle stereo camera. Our approach divides the whole map into local sub-maps identified by the so-called fingerprints (vehicle poses). At the sub-map level (low level SLAM), 3D sequential mapping of natural landmarks and the robot location/orientation are obtained using a top-down Bayesian method to model the dynamic behavior. A higher topological level (high level SLAM) based on fingerprints has been added to reduce the global accumulated drift, keeping real-time constraints. Using this hierarchical strategy, we keep the local consistency of the metric sub-maps, by mean of the EKF, and global consistency by using the topological map and the MultiLevel Relaxation (MLR) algorithm. Some experimental results for different large-scale outdoor environments are presented, showing an almost constant processing time.
    Robotics and Autonomous Systems. 01/2010;
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    ABSTRACT: The main objective of this work is to develop a robots' fleet working together to make assistance tasks in a hospital, geriatric or home in a collaborative way. This paper focus on multi-robot mapping and localization system for robotic assistants and a camera based system for tracking people. This paper presents a method to detect, recognize and track people using mount cameras fixed on a building and an algorithm for collaborative mobile robot mapping and localization based on probabilistic methods. The performance of this system has been tested successfully. Some experimental results and conclusions are presented.
    Robotics and Biomimetics, 2008. ROBIO 2008. IEEE International Conference on; 03/2009
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    ABSTRACT: This paper presents an automatic training method based on the Baum–Welch algorithm (also known as EM algorithm) and a robust low-level controller. The method has been applied to the indoor autonomous navigation of a surveillance robot that utilizes a WiFi+Ultrasound Partially Observable Markov Decision Process (POMDP). This method uses a robust local navigation system to automatically provide some WiFi+Ultrasound maps. These maps could be employed within probabilistic global robot localization systems. These systems use a priori probabilistic map in order to estimate the global robot position. The method has been tested in a real environment using two commercial Pioneer 2AT robotic platforms in the premises of the Department of Electronics at the University of Alcalá. Some experimental results and conclusions are presented.
    Robotica 01/2009; 27:1049-1061. · 0.88 Impact Factor
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    ABSTRACT: In this paper we present a new real-time hierarchical (topological/metric) Visual SLAM system focusing on the localization of a vehicle in large-scale outdoor urban environments. It is exclusively based on the visual information provided by both a low-cost wide-angle stereo camera and a low-cost GPS. Our approach divides the whole map into local sub-maps identified by the so-called fingerprint (reference poses). At the sub-map level (Low Level SLAM), 3D sequential mapping of natural landmarks and the vehicle location/orientation are obtained using a top-down Bayesian method to model the dynamic behavior. A higher topological level (High Level SLAM) based on references poses has been added to reduce the global accumulated drift, keeping real-time constraints. Using this hierarchical strategy, we keep local consistency of the metric sub-maps, by mean of the EKF, and global consistency by using the topological map and the MultiLevel Relaxation (MLR) algorithm. GPS measurements are integrated at both levels, improving global estimation. Some experimental results for different large-scale urban environments are presented, showing an almost constant processing time.
    2009 IEEE International Conference on Robotics and Automation, ICRA 2009, Kobe, Japan, May 12-17, 2009; 01/2009
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    ABSTRACT: This paper presents a new real-time hierarchical (topological/metric) simultaneous localization and mapping (SLAM) system. It can be applied to the robust localization of a vehicle in large-scale outdoor urban environments, improving the current vehicle navigation systems, most of which are only based on Global Positioning System (GPS). Then, it can be used on autonomous vehicle guidance with recurrent trajectories (bus journeys, theme park internal journeys, etc.). It is exclusively based on the information provided by both a low-cost, wide-angle stereo camera and a low-cost GPS. Our approach divides the whole map into local submaps identified by the so-called fingerprints (vehicle poses). In this submap level (low-level SLAM), a metric approach is carried out. There, a 3-D sequential mapping of visual natural landmarks and the vehicle location/orientation are obtained using a top-down Bayesian method to model the dynamic behavior. GPS measurements are integrated within this low-level improving vehicle positioning. A higher topological level (high-level SLAM) based on fingerprints and the multilevel relaxation (MLR) algorithm has been added to reduce the global error within the map, keeping real-time constraints. This level provides nearly consistent estimation, keeping a small degradation with GPS unavailability. Some experimental results for large-scale outdoor urban environments are presented, showing an almost constant processing time.
    IEEE Transactions on Intelligent Transportation Systems 01/2009; 10:440-452. · 3.06 Impact Factor
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    ABSTRACT: This paper presents a multi-robot mapping and localization system. Learning maps and efficient exp loration of unknown environment is a fundamental problem in mobile robotics usually called SLAM (simultaneous localization and mapping problem). Our approach uses a team of mobile robots which uses scan-matching and fast-slam techniques for mapping. The single maps generates for each robot are more accurate than map generated by odometry data. When a robot detects another, send its processed map and the master robot can generates a global map very accurate. This way, time necessary to build the global map is reduced.
    01/2008;
  • WAF2008. 01/2008;
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    ABSTRACT: This paper presents a new method for real-time SLAM calculation applied to autonomous robot navigation in large-scale environments without restrictions. It is exclusively based on the visual information provided by a cheap wide-angle stereo camera. Our approach divide the global map into local sub-maps identified by the so-called SIFT fingerprint. At the sub-map level (low level SLAM), 3D sequential mapping of natural land-marks and the robot location/orientation are obtained using a top-down Bayesian method to model the dynamic behavior. A high abstraction level to reduce the global accumulated drift, keeping real-time constraints, has been added (high level SLAM). This uses a correction method based on the SIFT fingerprints taking for each sub-map. A comparison of the low SLAM level using our method and SIFT features has been carried out. Some experimental results using a real large environment are presented.
    Intelligent Signal Processing, 2007. WISP 2007. IEEE International Symposium on; 11/2007
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    ABSTRACT: The main objective of this work is to develop a robots' fleet working together to make assistance tasks in a hospital or at home in a collaborative way. This paper presents a method to detect, recognize and track people using mount cameras fixed on a building and an algorithm for collaborative mobile robot localization based on probabilistic methods (Monte Carlo localization). The performance of this system has been tested successfully. Some experimental results and conclusions are presented.
    Intelligent Signal Processing, 2007. WISP 2007. IEEE International Symposium on; 11/2007
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    ABSTRACT: This paper presents a new method for real-time SLAM calculation applied to autonomous robot navigation in large-scale environments without restrictions. It is exclusively based on the visual information provided by a cheap wide-angle stereo camera. Our approach divide the global map into local sub-maps identified by the so-called SIFT fingerprint. At the sub-map level (low level SLAM), 3D sequential mapping of natural land-marks and the robot location/orientation are obtained using a top-down Bayesian method to model the dynamic behavior. A high abstraction level to reduce the global accumulated drift, keeping real-time constraints, has been added (high level SLAM). This uses a correction method based on the SIFT fingerprints taking for each sub-map. A comparison of the low SLAM level using our method and SIFT features has been carried out. Some experimental results using a real large environment are presented.
    Computer Aided Systems Theory - EUROCAST 2007, 11th International Conference on Computer Aided Systems Theory, Las Palmas de Gran Canaria, Spain, February 12-16, 2007, Revised Selected Papers; 01/2007
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    ABSTRACT: This paper presents a new method for real-time SLAM calculation applied to autonomous robot navigation in large environments without restrictions. It is exclusively based on the information provided by a cheap wide-angle stereo camera. Our approach divide the global map into local sub- maps identified by the so-called SIFT fingerprint. At the sub- map level (low level SLAM), 3D sequential mapping of natural land-marks and the robot location/orientation are obtained using a top-down Bayesian method to model the dynamic behavior. A high abstraction level to reduce the global accumulated drift, keeping real-time constraints, has been added (high level SLAM). This uses a SIFT correction method based on the sub-maps' fingerprints. A comparison of the low SLAM level using our method and SIFT features has been carried out. Some experimental results using a real large environment are presented.
    2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, October 29 - November 2, 2007, Sheraton Hotel and Marina, San Diego, California, USA; 01/2007
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    ABSTRACT: This paper presents a new method for real-time ego-motion calculation applied to the location/orientation of a cheap wide-angle stereo camera in a 3D environment. The objective is to apply it to a mobile robot navigation system. To achieve that, the goal is to solve the simultaneous localization and mapping (SLAM) problem. Our approach consists in the 3D sequential mapping of natural landmarks by means of a stereo camera, which also provides means to obtain the camera location/orientation. The dynamic behavior is modeled using a top-down Bayesian method. The results show a comparison between our system and a monocular visual SLAM system using a hand-waved camera. Several improvements related to no priori environment knowledge requirements, lower processing time (real-time constrained) and higher robustness is presented
    Intelligent Robots and Systems, 2006 IEEE/RSJ International Conference on; 11/2006
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    ABSTRACT: This paper presents a new method for real-time ego-motion calculation applied to the location/orientation of a cheap wide-angle stereo camera in a 3D environment. To achieve that, the goal is to solve the Simultaneous Localization and Mapping (SLAM) problem. Our approach consists in the 3D sequential mapping of natural land-marks by means of a stereo camera, which also provides means to obtain the camera location/orientation. The dynamic behavior is modeled using a top-down Bayesian method. The results show a comparison between our system and a monocular visual SLAM system using a hand-waved camera. Several improvements related to no priori environment knowledge requirements, lower processing time (real-time constrained) and higher robustness is presented.
    Distributed Intelligent Systems: Collective Intelligence and Its Applications, 2006. DIS 2006. IEEE Workshop on; 07/2006
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    D. Schleicher, L.M. Bergasa, R. Barea, E. Lopez
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    ABSTRACT: This paper presents a method to detect, recognize and track people using mount cameras fixed on a building. The method consists of two independent stages. One is dedicated to detect and track any moving object within the image frame. The other one is in charge to discard any moving object that is not a human being. To perform the first task, a particle filter algorithm is used, in such way that it can perform the tracking of multiple objects. For the recognition stage a PCA (principal components analysis) method is applied to several body parts (head, arms, etc.) respecting their geometrical constraints. The performance of the system has been tested successfully. Some experimental results and conclusions are presented
    Computer as a Tool, 2005. EUROCON 2005.The International Conference on; 02/2005
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    ABSTRACT: In this paper we present a 6DOF metric SLAM system for outdoor enviroments using a stereo camera, mounted next to the rear view mirror, as the only sensor. By means of SLAM the vehicle global position and a sparse map of natural landmarks are both estimated at the same time. The system combines both bearing and depth information using two different types of feature parametrization: inverse depth and 3D. Through this approach near and far features can be mapped, providing orientation and depth information respectively. Natural landmarks are extracted from the image and are stored as 3D or inverse depth points, depending on a depth thresh-old. At the moment each landmark is initialized, the normal of the patch surface is computed using the information of the stereo pair. In order to improve long-term tracking a 2D warping is done considering the normal vector information of each patch. This Visual SLAM system is focused on the localization of a vehicle in outdoor urban environments and can be fused with other cheap sensors such as GPS, so as to produce accurate estimations of vehicle's localization in a road. Some experimental results under outdoor environments and conclusions are presented.