Research Interests:
Mobile robot navigation in unknown environments requires the concurrent estimation of the mobile robot localization with respect to a base reference and the construction of a global map of the navigation area. In this paper we present a... more
Mobile robot navigation in unknown environments requires the concurrent estimation of the mobile robot localization with respect to a base reference and the construction of a global map of the navigation area. In this paper we present a comparative study of the performance of the localization and map building processes using two distinct sensorial systems: a rotating 2D laser rangefinder, and a trinocular stereo vision system.
Research Interests:
During mobile robot navigation, position estimates obtained by odometry drift with time, therefore becoming unrealistic and useless. This work enhances the use of external mechanisms by considering a multisensor system, composed of a 2D... more
During mobile robot navigation, position estimates obtained by odometry drift with time, therefore becoming unrealistic and useless. This work enhances the use of external mechanisms by considering a multisensor system, composed of a 2D laser rangefinder and an off-the-shelf CCD camera, which provides redundancy and assures reliability and precision of the observed features. We simultaneously consider both the map building and the localization problems using a state vector approach, which is related to the location estimations of both the robot and the map features, whilst its covariance matrix reflects the relationships between them. Relevance and importance of its off-diagonal elements is demonstrated by their contributions to “backwards estimations” whenever the vehicle returns to places in the navigation area which have been already visited and learned. Real experiments are presented, considering a LabMate mobile robot navigating in an static indoor environment
Research Interests: Localization, Path planning, Navigation, Automation, Mobile Robots, and 15 moreReliability, Sensor Fusion, Redundancy, Mobile Robot, Intelligent robots, Mobile Robot Navigation, CCD camera, Stochastic Resonance, Position Estimation, Covariance Matrix, Path Planning, Indoor Environment, Map Building, Odometry, and Location Estimation
We present a comparative study of the performance of map-based robot localisation processes based on diverse sensing devices such as monocular and trinocular vision systems and laser rangefinders. We study both the precision (error with... more
We present a comparative study of the performance of map-based robot localisation processes based on diverse sensing devices such as monocular and trinocular vision systems and laser rangefinders. We study both the precision (error with respect to the true values) and robustness (sensor measurements correctly paired with map features) of each localisation process. The experiment design we used allows one to compare these processes under exactly the same conditions. We conclude that comparable precision levels can be attained with each of the three sensors. With respect to robustness, monocular and trinocular vision pose more complex matching problems than laser, requiring more elaborate solutions to make the process robust
Research Interests:
Research Interests:
Research Interests:
Robust mobile robot localization requires the availability of highly reliable features obtained by the external sensors of the robot. Redundancy assures reliability and precision of the observed features. In this work we use two different... more
Robust mobile robot localization requires the availability of highly reliable features obtained by the external sensors of the robot. Redundancy assures reliability and precision of the observed features. In this work we use two different sensors, namely, a laser rangefinder and a monocular vision system, whose complementary nature allows one to robustly identify high level features, i.e. corners and semiplanes, in the environment of the robot. We present a general fusion mechanism, based on the extended information filter, supported by a robust modelling of uncertain geometric information, to fuse information obtained by different sensors mounted on the robot. Localization of the robot is achieved by matching these observations with an a priori map of the environment. An a priori estimation of the robot location is not required. Experimental results are presented, showing the increase in reliability of the observed features after fusing information from both sensors
Research Interests:
Research Interests:
Research Interests:
Abstract The ability to reconsider information over time allows to detect failures and is crucial for long term robust autonomous robot applications. This applies to loop closure decisions in localization and mapping systems. This paper... more
Abstract The ability to reconsider information over time allows to detect failures and is crucial for long term robust autonomous robot applications. This applies to loop closure decisions in localization and mapping systems. This paper describes a method to analyze all available information up to date in order to robustly remove past incorrect loop closures from the optimization process. The main novelties of our algorithm are: 1. incrementally reconsidering loop closures and 2.
Abstract In this paper we describe an algorithm to compute cycle constraints that can be used in many graph-based SLAM algorithms; we exemplify it in hierarchical SLAM. Our algorithm incrementally computes the minimum cycle basis of... more
Abstract In this paper we describe an algorithm to compute cycle constraints that can be used in many graph-based SLAM algorithms; we exemplify it in hierarchical SLAM. Our algorithm incrementally computes the minimum cycle basis of constraints from which any other cycle can be derived. Cycles in this basis are local and of minimum length, so that the associated cycle constraints have less linearization problems.
Abstract In this report we describe the main operations involved when predicting the map features in order to carry out an active search and a state vector updating when using a 6DOF SLAM system. Our Visual SLAM system combines depth... more
Abstract In this report we describe the main operations involved when predicting the map features in order to carry out an active search and a state vector updating when using a 6DOF SLAM system. Our Visual SLAM system combines depth points and inverse depth points using images gathered with a stereo camera which moves following a constant velocity model with zero mean Gaussian noise in the linear and angular accelerations.
Abstract We propose a place recognition algorithm for simultaneous localization and mapping (SLAM) systems using stereo cameras that considers both appearance and geometric information of points of interest in the images. Both near and... more
Abstract We propose a place recognition algorithm for simultaneous localization and mapping (SLAM) systems using stereo cameras that considers both appearance and geometric information of points of interest in the images. Both near and far scene points provide information for the recognition process. Hypotheses about loop closings are generated using a fast appearance-only technique based on the bag-of-words (BoW) method.
Abstract Place recognition is a challenging task in any SLAM system. Algorithms based on visual appearance are becoming popular to detect locations already visited, also known as loop closures, because cameras are easily available and... more
Abstract Place recognition is a challenging task in any SLAM system. Algorithms based on visual appearance are becoming popular to detect locations already visited, also known as loop closures, because cameras are easily available and provide rich scene detail. These algorithms typically result in pairs of images considered depicting the same location. To avoid mismatches, most of them rely on epipolar geometry to check spatial consistency.
Abstract A person buried by a snow avalanche can be found by measuring the magnetic field generated by an avalanche beacon or ARVA carried by the victim. However, the signals received are difficult to interpret and require people with... more
Abstract A person buried by a snow avalanche can be found by measuring the magnetic field generated by an avalanche beacon or ARVA carried by the victim. However, the signals received are difficult to interpret and require people with good training on the actual searching techniques. In this paper we show that the search can be automated using SLAM techniques.
Abstract: In this paper we show how to carry out robust place recognition using both near and far information provided by a stereo camera. Visual appearance is known to be very useful in place recognition tasks. In recent years, it has... more
Abstract: In this paper we show how to carry out robust place recognition using both near and far information provided by a stereo camera. Visual appearance is known to be very useful in place recognition tasks. In recent years, it has been shown that taking geometric information also into account further improves system robustness. Stereo visual systems provide 3D information and texture of nearby regions, as well as an image of far regions.
Abstract—This paper describes how multisensor fusion increases both reliability and precision of the environmental observations used for the simultaneous localization and map-building problem for mobile robots. Multisensor fusion is... more
Abstract—This paper describes how multisensor fusion increases both reliability and precision of the environmental observations used for the simultaneous localization and map-building problem for mobile robots. Multisensor fusion is performed at the level of landmarks, which represent sets of related and possibly correlated sensor observations. The work emphasizes the idea of partial redundancy due to the different nature of the information provided by different sensors.
RESUMEN En este trabajo se presentan los resultados de un sistema de localización y mapeo simultáneo desarrollado para un robot humanoide con base en información sensorial producida por un sistema de visión monocular (VSLAM).
Abstract The authors discuss the problem of selecting a sensor and its location in order to observe some object feature, with the goal of verifying an object-location hypothesis or refining its estimated location. The contribution of a... more
Abstract The authors discuss the problem of selecting a sensor and its location in order to observe some object feature, with the goal of verifying an object-location hypothesis or refining its estimated location. The contribution of a sensor observation as a function which relates the involved geometric feature and sensor capabilities with its impact in the reduction of uncertainty in the location of the object is analytically characterized.
3. Conclusions In addition to the classical LQ control strategy, a new approach based on controllability theory has been implemented for the automatic simulation of a depth change manoeuverability for an SSK submarine. The main... more
3. Conclusions In addition to the classical LQ control strategy, a new approach based on controllability theory has been implemented for the automatic simulation of a depth change manoeuverability for an SSK submarine. The main differences between both methodologies follow:(1) The controls obtained from the LQ controller appear in a feedback form. So, from a practical point of view it is necessary to complete the control system with a suitable Kalman filter to correct the data of the state provided by the sensors of the submarine.
Abstract—Long term autonomy in robots requires the ability to reconsider previously taken decisions when new evidence becomes available. Loop closing links generated by a place recognition system may become inconsistent as additional... more
Abstract—Long term autonomy in robots requires the ability to reconsider previously taken decisions when new evidence becomes available. Loop closing links generated by a place recognition system may become inconsistent as additional evidence arrives. This paper is concerned with the detection and exclusion of such contradictory information from the map being built, in order to recover the correct map estimate.
Abstract This paper addresses the problem of path planning considering uncertainty criteria over the belief space. Specifically, we propose a path planning algorithm that uses a novel determinant-based measure of uncertainty and a reduced... more
Abstract This paper addresses the problem of path planning considering uncertainty criteria over the belief space. Specifically, we propose a path planning algorithm that uses a novel determinant-based measure of uncertainty and a reduced representation of the environment, in order to obtain the minimum uncertainty path from a roadmap. Our proposal does not require a priori knowledge of the environment due to the construction of the roadmap via a graph-based SLAM algorithm.
In this paper1 we describe the Combined Kalman-Information Filter SLAM algorithm (CF SLAM), a judicious combination of Extended Kalman (EKF) and Extended Information Filters (EIF) that can be used to execute highly efficient SLAM in large... more
In this paper1 we describe the Combined Kalman-Information Filter SLAM algorithm (CF SLAM), a judicious combination of Extended Kalman (EKF) and Extended Information Filters (EIF) that can be used to execute highly efficient SLAM in large environments. CF SLAM is always more efficient than any other EKF or EIF algorithm: filter updates can be executed in as low as O (logn) as compared with O (n2) for Map Joining SLAM, O (n) for Divide and Conquer (D&C) SLAM, and the Sparse Local Submap Joining Filter (SLSJF).
Simultaneous Localization and Mapping, or SLAM, is a problem in the field of autonomous vehicles. Its solution, only found in the last decade, has been called “a 'Holy Grail'of the autonomous vehicle research community”[3].
Abstract: This article describes an experiment using a mobile robot equipped with a laser range finder and a trinocular vision system, designed to be specially well suited to test multisensor robot localization and map building... more
Abstract: This article describes an experiment using a mobile robot equipped with a laser range finder and a trinocular vision system, designed to be specially well suited to test multisensor robot localization and map building strategies. A pair of theodolites were used to obtain a ground-true solution to be compared with the results obtained by processing sensor information. Experimental result are reported focusing on the mobile robot localization and map building problems.
Abstract Presents a method to characterize the relevance of a pairing between a sensor observation and a model feature in determining the identity and location of the object to which it belongs. This relevance measurement expresses how... more
Abstract Presents a method to characterize the relevance of a pairing between a sensor observation and a model feature in determining the identity and location of the object to which it belongs. This relevance measurement expresses how much information the pairing contributes to support the object location hypothesis, and can be computed for any type of geometric element, obtained by any type of sensor.
Abstract We present a comparative study of the performance of map-based robot localisation processes based on diverse sensing devices such as monocular and trinocular vision systems and laser rangefinders. We study both the precision... more
Abstract We present a comparative study of the performance of map-based robot localisation processes based on diverse sensing devices such as monocular and trinocular vision systems and laser rangefinders. We study both the precision (error with respect to the true values) and robustness (sensor measurements correctly paired with map features) of each localisation process. The experiment design we used allows one to compare these processes under exactly the same conditions.
Abstract We present a method for solving the first location problem using 2D laser and vision. Our observation is a two-dimensional laser scan together with its corresponding image. The observation is segmented into textured vertical... more
Abstract We present a method for solving the first location problem using 2D laser and vision. Our observation is a two-dimensional laser scan together with its corresponding image. The observation is segmented into textured vertical planes; each vertical plane contains geometrical information about its location given by the laser scan, plus the gray level image obtained by the camera. The rich plane texture allows a safe plane recognition.
Abstract This paper describes a real-time implementation of feature-based concurrent mapping and localization (CML) running on a mobile robot in a dynamic indoor environment.
Loop closure detection systems for monocular SLAM come in three broad categories:(i) map-to-map,(ii) image-to-image and (iii) image-to-map. In this paper, we have chosen an implementation of each and performed experiments allowing the... more
Loop closure detection systems for monocular SLAM come in three broad categories:(i) map-to-map,(ii) image-to-image and (iii) image-to-map. In this paper, we have chosen an implementation of each and performed experiments allowing the three approaches to be compared. The sequences used include both indoor and outdoor environments and single and multiple loop trajectories.
In this paper we study the Extended Kalman Filter approach to simultaneous localization and mapping (EKF-SLAM), describing its known properties and limitations, and concentrate on the filter consistency issue. We show that linearization... more
In this paper we study the Extended Kalman Filter approach to simultaneous localization and mapping (EKF-SLAM), describing its known properties and limitations, and concentrate on the filter consistency issue. We show that linearization of the inherent nonlinearities of both the vehicle motion and the sensor models frequently drives the solution of the EKF-SLAM out of consistency, specially in those situations where uncertainty surpasses a certain threshold.
Abstract In this paper we describe divide and conquer SLAM (D&C SLAM), an algorithm for performing simultaneous localization and mapping using the extended Kalman filter.
1. Executive summary This document describes the activities performed in WP3 to validate the quality of the datasets obtained in the RAWSEEDS project. Most of the effort in WP3 has been devoted to the design of the algorithms for data... more
1. Executive summary This document describes the activities performed in WP3 to validate the quality of the datasets obtained in the RAWSEEDS project. Most of the effort in WP3 has been devoted to the design of the algorithms for data validation and their software implementation. Having prepared the data validation software ahead of time is allowing quick progress in WP3.
Abstract—In this paper we describe the Combined Filter, a judicious combination of Extended Kalman (EKF) and Extended Information filters (EIF) that can be used to execute highly efficient SLAM in large environments. With the CF, filter... more
Abstract—In this paper we describe the Combined Filter, a judicious combination of Extended Kalman (EKF) and Extended Information filters (EIF) that can be used to execute highly efficient SLAM in large environments. With the CF, filter updates can be executed in as low as O (log n) as compared with other EKF and EIF based algorithms: O (n 2) for Map Joining SLAM, O (n) for Divide and Conquer (D&C) SLAM, and O (n 1. 5) for the Sparse Local Submap Joining Filter (SLSJF).
The concept of autonomy of mobile robots encompasses many areas of knowledge, methods and ultimately algorithms designed for trajectory control, obstacle avoidance, localization, map building, and so forth. Practically, the success of a... more
The concept of autonomy of mobile robots encompasses many areas of knowledge, methods and ultimately algorithms designed for trajectory control, obstacle avoidance, localization, map building, and so forth. Practically, the success of a path planning and navigation mission of an autonomous vehicle depends on the availability of both a sufficiently reliable estimation of the vehicle location, and an accurate representation of the navigation area.
This volume is one of two special journal issues compiled from the best papers presented at the fourth Robotics: Science and Systems (RSS) conference, held in Zurich in June 2008. As indicated by its title, RSS is a meeting that values... more
This volume is one of two special journal issues compiled from the best papers presented at the fourth Robotics: Science and Systems (RSS) conference, held in Zurich in June 2008. As indicated by its title, RSS is a meeting that values research papers focusing on the development of, and experimentation with, new complex robotic systems in real-world environments as much as it values new theoretical and algorithmic advances. The second special issue will be published by the International Journal of Robotics Research.
Abstract In this paper, we show that all processes associated with the move-sense-update cycle of extended Kalman filter (EKF) Simultaneous Localization and Mapping (SLAM) can be carried out in time linear with the number of map features.... more
Abstract In this paper, we show that all processes associated with the move-sense-update cycle of extended Kalman filter (EKF) Simultaneous Localization and Mapping (SLAM) can be carried out in time linear with the number of map features. We describe Divide and Conquer SLAM, which is an EKF SLAM algorithm in which the computational complexity per step is reduced from O (n 2) to O (n), and the total cost of SLAM is reduced from O (n 3) to O (n 2).
Abstract: The extraction of reliable features is a key issue for autonomous underwater vehicle navigation. Imaging sonars can produce acoustic images of the surroundings of the vehicle. Despite of the noise, the phantom echoes and... more
Abstract: The extraction of reliable features is a key issue for autonomous underwater vehicle navigation. Imaging sonars can produce acoustic images of the surroundings of the vehicle. Despite of the noise, the phantom echoes and reflections, we believe that they are a good source for features since they can work in turbid water where other sensors like vision fail. Moreover, they can cover wide areas incrementing the number of features visible within a scan.
Abstract—This paper describes a system for performing multi-session visual mapping in large-scale environments. Multi-session mapping considers the problem of combining the results of multiple Simultaneous Localisation and Mapping (SLAM)... more
Abstract—This paper describes a system for performing multi-session visual mapping in large-scale environments. Multi-session mapping considers the problem of combining the results of multiple Simultaneous Localisation and Mapping (SLAM) missions performed repeatedly over time in the same environment. The goal is to robustly combine multiple maps in a common metrical coordinate system, with consistent estimates of uncertainty.
Abstract In this paper we show that all processes associated to the move-sense-update cycle of EKF SLAM can be carried out in time linear in the number of map features. We describe Divide and Conquer SLAM, an EKF SLAM algorithm where the... more
Abstract In this paper we show that all processes associated to the move-sense-update cycle of EKF SLAM can be carried out in time linear in the number of map features. We describe Divide and Conquer SLAM, an EKF SLAM algorithm where the computational complexity per step is reduced from O (n2) to O (n)(the total cost of SLAM is reduced from O (n3) to O (n2)).
Abstract A general procedure to compute and validate geometric constraints between uncertain geometric features is presented. Uncertain geometric information is represented using the symmetries and perturbation model. The procedure... more
Abstract A general procedure to compute and validate geometric constraints between uncertain geometric features is presented. Uncertain geometric information is represented using the symmetries and perturbation model. The procedure obtains the geometric relations between any pair of geometric elements in a systematic way, with an explicit consideration of the uncertainty due to the use of different sensors.
Abstract Robust mobile robot localization requires the availability of highly reliable features obtained by the external sensors of the robot. Redundancy assures reliability and precision of the observed features. In this work we use two... more
Abstract Robust mobile robot localization requires the availability of highly reliable features obtained by the external sensors of the robot. Redundancy assures reliability and precision of the observed features. In this work we use two different sensors, namely, a laser rangefinder and a monocular vision system, whose complementary nature allows one to robustly identify high level features, ie corners and semiplanes, in the environment of the robot.
Abstract—We present a place recognition algorithm for SLAM systems using stereo cameras that considers both appearance and geometric information. Both near and far scene points provide information for the recognition process. Hypotheses... more
Abstract—We present a place recognition algorithm for SLAM systems using stereo cameras that considers both appearance and geometric information. Both near and far scene points provide information for the recognition process. Hypotheses about loop closings are generated using a fast appearance technique based on the bag-of-words (BoW) method. Loop closing candidates are evaluated in the context of recent images in the sequence.
Abstract We present the two-dimensional (2-D) version of the symmetries and perturbation model (SPmodel), a probabilistic representation model and an extended Kalman filter integration mechanism for uncertain geometric information that is... more
Abstract We present the two-dimensional (2-D) version of the symmetries and perturbation model (SPmodel), a probabilistic representation model and an extended Kalman filter integration mechanism for uncertain geometric information that is suitable for sensor fusion and integration in multisensor systems. We apply the SPmodel to the problem of location estimation in indoor mobile robotics, experimenting with the mobile robot MACROBE.