An experimental comparison of localization methods

Gutmann, Jens-Steffen; Burgard, Wolfram; Fox, Dieter; Konolige, Kurt

doi:10.1109/iros.1998.727280

Cited by 191 publications

(104 citation statements)

References 14 publications

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“…Here, they concentrate on robot skills and architecture but they do not take in account how to systematically evaluate the robot performance in a changing environment. Gutmann et al [12] presented a set of extensive experiments evaluating the accuracy and robustness of localization systems using datasets. Calisi et al [5] propose a benchmark framework that concentrates only on the evalua-tion of vehicle motion algorithms.…”

Section: Related Workmentioning

confidence: 99%

An Experimental Protocol for Benchmarking Robotic Indoor Navigation

Sprunk

Röwekämper

Parent

et al. 2015

Experimental Robotics

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Abstract. Robot navigation is one of the most studied problems in robotics and the key capability for robot autonomy. Navigation techniques have become more and more reliable, but evaluation mainly focused on individual navigation components (i.e., mapping, localization, and planning) using datasets or simulations. The goal of this paper is to define an experimental protocol to evaluate the whole navigation system, deployed in a real environment. To ensure repeatability and reproducibility of experiments, our benchmark protocol provides detailed definitions and controls the environment dynamics. We define standardized environments and introduce the concept of a reference robot to allow comparison between different navigation systems at different experimentation sites. We present applications of our protocol in experiments in two different research groups, showing the usefulness of the benchmark.

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Section: Related Workmentioning

confidence: 99%

An Experimental Protocol for Benchmarking Robotic Indoor Navigation

Sprunk

Röwekämper

Parent

et al. 2015

Experimental Robotics

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“…Gutmann et al [4] and Arras et al [8] presented featurebased localization approaches. Here, a set of features is extracted out the range scan and matched to features contained in the representation of the environment.…”

Section: Related Workmentioning

confidence: 99%

“…Laser range sensors have been widely used for successful localization [4], [5], [6], [7], [8]. They provide distance and bearing information to objects in the environment.…”

Section: Introductionmentioning

confidence: 99%

Utilizing reflection properties of surfaces to improve mobile robot localization

Bennewitz

Stachniss

Behnke

et al. 2009

2009 IEEE International Conference on Robotics and Automation

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Abstract-A main difficulty that arises in the context of probabilistic localization is the design of an appropriate observation model, i.e., determining the likelihood of a sensor measurement given the pose of the robot and a map of the environment. Many successful approaches to localization rely on data provided by range sensors, e.g., laser range scanners. When using such data one normally has to deal with erroneous maximum-range readings that occur due to poor-reflecting surfaces. In general, these readings cannot be distinguished from readings obtained when no obstacle is within the measurement range of the sensor. Therefore, existing localization techniques treat these readings alike in the observation model. In this paper, we present a novel approach that explicitly considers the reflection properties of surfaces and thus the expectation of valid range measurements. In addition to the expected range measurement, we compute the probability of reflectance for a beam given the relative pose of the robot to the obstacle taking into account the angle of incidence of the beam. We estimate the reflection properties of surfaces using data collected with a mobile robot equipped with a laser range scanner. As we demonstrate in experiments carried out with a real robot, our technique leads to significantly improved localization results compared to a state-of-the-art observation model.

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“…In the Bayesian Filtering problem one has to calculate the probability density of the robot position . 1 For a complete review and evaluation of localisation method refer to the two enlightening works of Gutmann [10,11].…”

Section: The Proposed Kidnapping Strategymentioning

confidence: 99%

Image-Based Monte-Carlo Localisation without a Map

Menegatti

Zoccarato

Pagello

et al. 2003

AI*IA 2003: Advances in Artificial Intelligence

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Abstract. In this paper, we propose a way to fuse the image-based localisation approach with the Monte-Carlo localisation approach. The method we propose does not suffer of the major limitation of the two separated methods: the need of a metric map of the environment for the Monte-Carlo localisation and the failure of the image-based approach in environments with spatial periodicity (perceptual aliasing). The approach we developed exploits the properties of the Fourier Transform of the omnidirectional images and uses the similarity between the images to weights the beliefs about the robot position. Successful experiments in large indoor environment are presented in which we do not used a priory information on the metrical map of the environment.

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An experimental comparison of localization methods

Abstract: Abstract

Cited by 191 publications

References 14 publications

An Experimental Protocol for Benchmarking Robotic Indoor Navigation

An Experimental Protocol for Benchmarking Robotic Indoor Navigation

Utilizing reflection properties of surfaces to improve mobile robot localization

Image-Based Monte-Carlo Localisation without a Map

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