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ABSTRACT: 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.
04/2008: pages 287-296;
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ABSTRACT: This paper presents a robust algorithm for segmentation and line detection in 2D range scans. The described method exploits the multimodal probability density function of the residual error. It is capable of segmenting the range data in clusters, estimate the straight segments parameters, and estimate the scale of inliers error noise successfully, despite of high level of spurious data. No prior knowledge about the sensor and object properties is given to the algorithm. The mode seeking is based on mean shift algorithm, which has been widely used and tested in 3D laser scan segmentation, machine learning and pattern recognition applications. We show the reliability of the technique with experimental indoor and outdoor manmade environment. Compared with classical methods, a good compromise between false positive, false negative, wrong segment split and wrong segment merge is achieved, with improved accuracy in the estimated parameters
Intelligent Robots and Systems, 2006 IEEE/RSJ International Conference on; 11/2006
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ABSTRACT: This article describes a rigorous and complete framework for the simultaneous localization and map building problem for mobile robots: the symmetries and perturbations map (SPmap), which is based on a general probabilistic representation of uncertain geometric information. We present a complete experiment with a LabMate mobile robot navigating in a human-made indoor environment and equipped with a rotating two-dimensional (2-D) laser rangefinder. Experiments validate the appropriateness of our approach and provide a real measurement of the precision of the algorithms.
11/2003;
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ABSTRACT: 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.
11/2003;
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ABSTRACT: We describe a method for automatically generating accurate piecewise planar models for indoor scenes using a combination of a 2D laser scanner and a camera on a mobile platform. The method exploits the complementarity of the sensors. Mapping techniques applied to 2D laser scans simultaneously compute a map and the location of the sensor in the unknown environment. This provides an initial estimate for the vision algorithms by compensating the rotation, foreshortening and the scale change between images. The vision algorithms are then able to compute a very accurate registration (via a plane to plane homography), which is used to segment the model into planar facets, and to improve the estimate of the model and sensor position. Results are demonstrated on a man made scene using a 2D laser scanner and a calibrated camera mounted on a trolley.
Robotics and Automation, 2003. Proceedings. ICRA '03. IEEE International Conference on; 10/2003
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ABSTRACT: The estimation of the 2D relative motion of an indoor robot using monocular vision is presented. The camera calibration is known, and its motion is limited to be a planar one. These constraints are included in the robust regression of epipolar geometry from point matches. Motion is derived from the epipolar geometry. A sequence of 54 real images is used to test the algorithm. Accurate motion both in rotation and translation angles, of 0.4 and 1.7 deg, is successfully derived.
05/2003;
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[show abstract]
[hide abstract]
ABSTRACT: This article describes a rigorous and complete framework for the
simultaneous localization and map building problem for mobile robots:
the symmetries and perturbation map (SPmap), which is based on a general
probabilistic representation of uncertain geometric information. We
present a complete experiment with a LabMate<sup>TM</sup> mobile robot
navigating in a human-made indoor environment and equipped with a
rotating 2D laser rangefinder. Experiments validate the appropriateness
of our approach and provide a real measurement of the precision of the
algorithms
IEEE Transactions on Robotics and Automation 11/1999;
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ABSTRACT: This paper presents a method to perform a goal directed reactive
navigation in unknown indoor environments. Two sensors cooperate to
accomplish this task: trinocular vision and 3D laser rangefinder.
Trinocular vision selects the initial goal location for the navigation
task. Laser is used to accomplish a reactive navigation to avoid the
obstacles and to periodically relocate the goal with respect to the
robot, so the dead-reckoning drift is compensated. An extended Kalman
filter is used to solve the data association problem and to perform the
goal relocation while the robot navigates. Experimental results
involving a real mobile robot are presented, validating the proposed
method
Robotics and Automation, 1999. Proceedings. 1999 IEEE International Conference on; 02/1999
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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. 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
Robotics and Automation, 1999. Proceedings. 1999 IEEE International Conference on; 02/1999
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01/1999
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ABSTRACT: The validation of matching hypotheses using Mahalanobis distance is extensively utilized in robotic applications, and in general data-association techniques. The Mahalanobis distance, defined by the innovation and its covariance, is compared with a threshold defined by the chi-squared distribution to validate a matching hypothesis; the validation test is a time-consuming operation. This paper presents an efficient computation for this test.The validation test implies a computational overhead for two reasons: first, because of covariance matrix inversion, and second because the computation of the covariance and innovation terms are also expensive operations, in fact, more expensive than the inversion itself.The method described here can be summarized as an incremental, non-decreasing computation for the Mahalanobis distance; if the incrementally computed value exceeds the threshold then the computation is stopped. The elements of covariance and innovation, and the matrix inversion itself, are only computed if they are used; progressivity is the major advantage of the method. The method is based upon the square-root-free Cholesky’s factorization.In addition, a lower bound for the Mahalanobis distance is proposed. This lower bound has two advantages: it can be progressively computed, and it is greater than the classical trace lower bound.
Engineering Applications of Artificial Intelligence.
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ABSTRACT: A method to determine both the camera location and scene structure from image straight segment correspondences is presented. The proposed method considers the finite segment length in order to use stronger constraints than do those that use the infinite line that supports the image segment. The constraints between image segments involve a weak pairing between image segment midpoints. This allows deviations of the midpoint only in the segment direction. Experimental results are presented of structure and motion computations from the image straight line segment matching using two real images.
Pattern Recognition.
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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. Once two planes are recognized as correspondent, the computer vision geometry allows to compute the relative camera motion. The proposed algorithm outperforms both laser-only and vision-only algorithms. This is shown in the experimental results where a map composed of 8 observations of a 20×3 meter corridor is used to successfully locate the robot (without any other prior) in 163 out of 192 initial test robot locations.
Robotics and Automation, 2004. Proceedings. ICRA '04. 2004 IEEE International Conference on;
