sigmaMCL: Monte-Carlo Localization for Mobile Robots with Stereo Vision

Elinas, Pantelis; Little, James J.

doi:10.15607/rss.2005.i.049

Cited by 34 publications

(17 citation statements)

References 15 publications

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“…Mobile robot localization is a well studied field in robotics and several approaches to localization have been proposed in the past [1], [2], [7], [10], [15], [19]. Probabilistic approaches have been successfully applied to localize robots with respect to a given map in a robust manner, often relying on techniques such as extended Kalman filters (EKF) [19], histogram filters [13] or particle filters, often referred to as Monte-Carlo localization (MCL) [8].…”

Section: Related Workmentioning

confidence: 99%

“…Commonly used sensors for vehicle localization are cameras [1], [2], [10], [15], [22], RFID or wireless receivers estimating radio signal strength [11], [9], laser scanners [8], [17] or GPS receivers. Vision-based MCL was first introduced by Dellaert et al [7].…”

Section: Related Workmentioning

confidence: 99%

“…Se et al [22] were the first to perform localization using SIFT descriptor matching but did not track the position of the robot over time. Elinas and Little [10] present a MCL-based system using stereo cameras and SIFT features whereas Bennewitz et al [2] work with monocular cameras and do not require explicit data associations among features.…”

Section: Related Workmentioning

confidence: 99%

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On the position accuracy of mobile robot localization based on particle filters combined with scan matching

Röwekämper

Sprunk

Tipaldi

et al. 2012

2012 IEEE/RSJ International Conference on Intelligent Robots and Systems

113

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Abstract-Many applications in mobile robotics and especially industrial applications require that the robot has a precise estimate about its pose. In this paper, we analyze the accuracy of an integrated laser-based robot pose estimation and positioning system for mobile platforms. For our analysis, we used a highly accurate motion capture system to precisely determine the error in the robot's pose. We are able to show that by combining standard components such as Monte-Carlo localization, KLD sampling, and scan matching, an accuracy of a few millimeters at taught-in reference locations can be achieved. We believe that this is an important analysis for developers of robotic applications in which pose accuracy matters.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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On the position accuracy of mobile robot localization based on particle filters combined with scan matching

Röwekämper

Sprunk

Tipaldi

et al. 2012

2012 IEEE/RSJ International Conference on Intelligent Robots and Systems

113

View full text Add to dashboard Cite

show abstract

“…They did not apply a technique to track the position of the robot over time. Recently, Elinas and Little [7] presented a system that uses MCL in combination with a database of SIFT features learned in the same restricted environment. All these approaches use stereo vision to compute the 3D position of a landmark and match the visual features in the current view to all those in the database to find correspondences.…”

Section: Related Workmentioning

confidence: 99%

“…Whereas existing systems, that perform metric localization and mapping using SIFT features, apply stereo vision in order to compute the 3D position of the features [20,7,21, 2], we rely on a single camera only during localization. Since we want to concentrate on the localization aspect, we facilitate the map acquisition process by using a robot equipped with a camera and a proximity sensor.…”

Section: Introductionmentioning

confidence: 99%

Metric Localization with Scale-Invariant Visual Features Using a Single Perspective Camera

Bennewitz

Stachniss

Burgard

et al.

Springer Tracts in Advanced Robotics

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Abstract. The Scale Invariant Feature Transform (SIFT) has become a popular feature extractor for vision-based applications. It has been successfully applied to metric localization and mapping using stereo vision and omnivision. In this paper, we present an approach to Monte-Carlo localization using SIFT features for mobile robots equipped with a single perspective camera. First, we acquire a 2D grid map of the environment that contains the visual features. To come up with a compact environmental model, we appropriately down-sample the number of features in the final map. During localization, we cluster close-by particles and estimate for each cluster the set of potentially visible features in the map using ray-casting. These relevant map features are then compared to the features extracted from the current image. The observation model used to evaluate the individual particles considers the difference between the measured and the expected angle of similar features. In real-world experiments, we demonstrate that our technique is able to accurately track the position of a mobile robot. Moreover, we present experiments illustrating that a robot equipped with a different type of camera can use the same map of SIFT features for localization.

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Sliding window filter with application to planetary landing

Sibley

Matthies

Sukhatme

2010

Journal of Field Robotics

223

117

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We are concerned with improving the range resolution of stereo vision for entry, descent, and landing (EDL) missions to Mars and other planetary bodies. The goal is to create accurate and precise three-dimensional planetary surface-structure estimates by filtering sequences of stereo images taken from an autonomous landing vehicle. We describe a sliding window filter (SWF) approach based on delayed state marginalization. The SWF can run in constant time, yet still achieve experimental results close to those of the bundle adjustment solution. This technique can scale from the offline batch least-squares solution to fast online incremental solutions. For instance, if the window encompasses all time, the solution is equivalent to full bundle adjustment; if only one time step is maintained, the solution matches the extended Kalman filter; if poses and landmarks are slowly marginalized out over time such that the state vector ceases to grow, then the filter becomes constant time, like visual odometry. Within the constant time regime, the sliding window approach demonstrates convergence properties that are close to those of the full batch solution and strictly superior to visual odometry. Experiments with real data show that ground structure estimates follow the expected convergence pattern that is predicted by theory. These experiments indicate the effectiveness of filtering long-range stereo for EDL. C 2010 Wiley Periodicals, Inc.

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sigmaMCL: Monte-Carlo Localization for Mobile Robots with Stereo Vision

Cited by 34 publications

References 15 publications

On the position accuracy of mobile robot localization based on particle filters combined with scan matching

On the position accuracy of mobile robot localization based on particle filters combined with scan matching

Metric Localization with Scale-Invariant Visual Features Using a Single Perspective Camera

Sliding window filter with application to planetary landing

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