Mobile Robot Localization and Mapping with Uncertainty using Scale-Invariant Visual Landmarks

Se, Stephen; Lowe, David; Little, James J.

doi:10.1177/027836402761412467

Cited by 546 publications

(199 citation statements)

References 22 publications

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“…Sometimes, the features were used to estimate relative movements [16,31,32], and other times, they were used to detect loop closures [10,17,33,34].…”

Section: Background and Relation To Slammentioning

confidence: 99%

Radar SLAM using visual features

Callmer

Törnqvist

Gustafsson

et al. 2011

EURASIP J. Adv. Signal Process.

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A vessel navigating in a critical environment such as an archipelago requires very accurate movement estimates. Intentional or unintentional jamming makes GPS unreliable as the only source of information and an additional independent supporting navigation system should be used. In this paper, we suggest estimating the vessel movements using a sequence of radar images from the preexisting body-fixed radar. Island landmarks in the radar scans are tracked between multiple scans using visual features. This provides information not only about the position of the vessel but also of its course and velocity. We present here a navigation framework that requires no additional hardware than the already existing naval radar sensor. Experiments show that visual radar features can be used to accurately estimate the vessel trajectory over an extensive data set.

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“…Sometimes, the features were used to estimate relative movements [16,31,32], and other times, they were used to detect loop closures [10,17,33,34].…”

Section: Background and Relation To Slammentioning

confidence: 99%

Radar SLAM using visual features

Callmer

Törnqvist

Gustafsson

et al. 2011

EURASIP J. Adv. Signal Process.

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“…Over the last decade, many appearance-based localization methods have been proposed (Owen and Nehmzow 1998;Franz et al 1998;Se et al 2002). SIFT (Scale Invariant Feature Transform) features (Lowe 2004) have been widely used for robot localization.…”

Section: Related Workmentioning

confidence: 99%

Robust vision-based robot localization using combinations of local feature region detectors

et al. 2009

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International audienceThis paper presents a vision-based approach for mobile robot localization. The model of the environment is topological. The new approach characterizes a place using a signature. This signature consists of a constellation of descriptors computed over different types of local afﬁne covariant regions extracted from an omnidirectional image acquired rotating a standard camera with a pan-tilt unit. This type of representation permits a reliable and distinctive environment modelling. Our objectives were to validate the proposed method in indoor environments and, also, to ﬁnd out if the combination of complementary local feature region detectors improves the localization versus using a single region detector. Our experimental results show that if false matches are effectively rejected, the combination of different covariant afﬁne region detectors increases notably the performance of the approach by combining the different strengths of the individual detectors. In order to reduce the localization time, two strategies are evaluated: re-ranking the map nodes using a global similarity measure and using standard perspective view ﬁeld of 45°. In order to systematically test topological localization methods, another contribution proposed in this work is a novel method to see the degradation in localization performance as the robot moves away from the point where the original signature was acquired. This allows to know the robustness of the proposed signature. In order for this to be effective, it must be done in several, variated, environments that test all the possible situations in which the robot may have to perform localization

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“…Davison (2003) efficiently estimated the location of a single camera and related visual features by means of feature tracking. Se et al (2002) used scale invariant feature transform (SIFT) features generated from a trinocular vision in an indoor environment and maintained the robot pose and the 3-D map of the features separately. The vSLAM (Karlsson et al 2005) was realized by using a single camera similarly to Se et al (2002).…”

Section: Introductionmentioning

confidence: 99%

“…Se et al (2002) used scale invariant feature transform (SIFT) features generated from a trinocular vision in an indoor environment and maintained the robot pose and the 3-D map of the features separately. The vSLAM (Karlsson et al 2005) was realized by using a single camera similarly to Se et al (2002). CV-SLAM (Jeong and Lee 2005) proposed SLAM and kidnapping solutions using a ceiling vision sensor which used Harris corners and their orientation information from ceiling and side walls.…”

Section: Introductionmentioning

confidence: 99%

A practical approach for EKF-SLAM in an indoor environment: fusing ultrasonic sensors and stereo camera

et al. 2008

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Improving the practical capability of SLAM requires effective sensor fusion to cope with the large uncertainties from the sensors and environment. Fusing ultrasonic and vision sensors possesses advantages of both economical efficiency and complementary cooperation. In particular, it can resolve the false data association and divergence problem of an ultrasonic sensor-only algorithm and overcome both the low frequency of SLAM update caused by the computational burden and the weakness to illumination changes of a vision sensor-only algorithm. In this paper, we propose a VR-SLAM (Vision and Range sensor-SLAM) algorithm to combine ultrasonic sensors and stereo camera very effectively. It consists of two schemes: (1) extracting robust point and line features from sonar data and (2) recognizing planar visual objects using a multi-scale Harris corner detector and its SIFT descriptor from a pre-constructed object database. We show that fusing these schemes through EKF-SLAM frameworks can achieve correct data association via the object recognition and high frequency update via the sonar features. The performance of the proposed algorithm was verified by experiments in various real indoor environments.

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Mobile Robot Localization and Mapping with Uncertainty using Scale-Invariant Visual Landmarks

Abstract: A key component of a mobile robot system is the ability to localize

Cited by 546 publications

References 22 publications

Radar SLAM using visual features

Radar SLAM using visual features

Robust vision-based robot localization using combinations of local feature region detectors

A practical approach for EKF-SLAM in an indoor environment: fusing ultrasonic sensors and stereo camera

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