Image-Based Monte-Carlo Localisation without a Map

Menegatti, Emanuele; Zoccarato, Mauro; Pagello, Enrico; Ishiguro, Hiroshi

doi:10.1007/978-3-540-39853-0_35

Cited by 12 publications

(2 citation statements)

References 17 publications

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“…Many approaches to this problem can be found in the literature. For instance, Menegatti et al (2003) and Lenser and Veloso (2000) propose to sample a portion of the particles directly form the posterior likelihood. Thrun et al (2000) introduce the Mixture-CML where normal sampling is used in conjunctions with a dual sampling where particles are drawn from the sensor model and then odometry is used to assess its compliance.…”

Section: The Auxiliary Particle Filtermentioning

confidence: 99%

Active Appearance-Based Robot Localization Using Stereo Vision

2005

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Abstract.A vision-based robot localization system must be robust: able to keep track of the position of the robot at any time even if illumination conditions change and, in the extreme case of a failure, able to efficiently recover the correct position of the robot. With this objective in mind, we enhance the existing appearance-based robot localization framework in two directions by exploiting the use of a stereo camera mounted on a pan-and-tilt device. First, we move from the classical passive appearance-based localization framework to an active one where the robot sometimes executes actions with the only purpose of gaining information about its location in the environment. Along this line, we introduce an entropy-based criterion for action selection that can be efficiently evaluated in our probabilistic localization system. The execution of the actions selected using this criterion allows the robot to quickly find out its position in case it gets lost. Secondly, we introduce the use of depth maps obtained with the stereo cameras. The information provided by depth maps is less sensitive to changes of illumination than that provided by plain images. The main drawback of depth maps is that they include missing values: points for which it is not possible to reliably determine depth information. The presence of missing values makes Principal Component Analysis (the standard method used to compress images in the appearance-based framework) unfeasible. We describe a novel Expectation-Maximization algorithm to determine the principal components of a data set including missing values and we apply it to depth maps. The experiments we present show that the combination of the active localization with the use of depth maps gives an efficient and robust appearance-based robot localization system.

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Section: The Auxiliary Particle Filtermentioning

confidence: 99%

Active Appearance-Based Robot Localization Using Stereo Vision

2005

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“…The similarity of images is determined by calculating the sum of the absolute difference of the most significant Fourier coefficients. Menegatti and colleagues [5] extend the method by Monte Carlo Localization to solve the problem of perceptual aliasing. That means the response of the current image to several reference images.…”

Section: Introductionmentioning

confidence: 99%

Illumination Insensitive Robot Self-Localization Using Panoramic Eigenspaces

Steinbauer

Bischof

2005

RoboCup 2004: Robot Soccer World Cup VIII

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We propose to use a robust method for appearance-based matching that has been shown to be insensitive to illumination and occlusion for robot self-localization. The drawback of this method is that it relies on panoramic images taken in one certain orientation, restricts the heading of the robot throughout navigation or needs additional sensors for orientation, e.g. a compass. To avoid these problems we propose a combination of the appearance-based method with odometry data. We demonstrate the robustness of the proposed self-localization against changes in illumination by experimental results obtained in the RoboCup Middle-Size scenario.

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Vision-Based Hybrid Map-Building and Robot Localization in Unstructured and Moderately Dynamic Environments

Rady

2016

Studies in Computational Intelligence

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Image-Based Monte-Carlo Localisation without a Map

Cited by 12 publications

References 17 publications

Active Appearance-Based Robot Localization Using Stereo Vision

Active Appearance-Based Robot Localization Using Stereo Vision

Illumination Insensitive Robot Self-Localization Using Panoramic Eigenspaces

Vision-Based Hybrid Map-Building and Robot Localization in Unstructured and Moderately Dynamic Environments

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