Practical Extensions to Vision-Based Monte Carlo Localization Methods for Robot Soccer Domain

Kaplan, Kemal; Celik, Buluc; Meriçli, Tekin; Meriçli, Çetin; Akın, H. Levent

doi:10.1007/11780519_62

Cited by 7 publications

(5 citation statements)

References 6 publications

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“…The data collected at each one of the five runs were manually analyzed to calculate the false positive and true positive responses of the system. In the second set of experiments effects of the proposed module on our current localization algorithm (Monte Carlo Based Localization) (Kaplan, K. et al, 2005) were tested 1 . Since in most robotic systems, the localization module is a mission critical module it was considered to be a good modality for our experiments.…”

Section: Methodsmentioning

confidence: 99%

“…The primary effect of our proposed post-perception module was the removal of misperceptions from the localization input. Particles of the Monte Carlo localization algorithm (Kaplan, K. et al, 2005) diverged in cases where misperceptions appeared consistently in the input of localization. In such cases our algorithm only used the odometry information to update the pose estimate, delaying effects of the divergence for a limited period of time.…”

Section: Localization Performance Experimentsmentioning

confidence: 99%

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Hidden Markov Model Based Visual Perception Filtering in Robotic Soccer

Kavaklioğlu

Akın

2009

International Journal of Advanced Robotic Systems

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Section: Methodsmentioning

confidence: 99%

Section: Localization Performance Experimentsmentioning

confidence: 99%

Hidden Markov Model Based Visual Perception Filtering in Robotic Soccer

Kavaklioğlu

Akın

2009

International Journal of Advanced Robotic Systems

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“…Percepts are commonly considered to be independent of each other to simplify computation, even if they are used for the same purpose, such as localization [10]. Using the distance of features detected 1-4244-0259-X/06/$20.00 C)2006 IEEE within a single camera image to improve Monte Carlo Localization was proposed by [5]: when two landmarks are detected simultaneously, the distance between them yields information about the robot's whereabouts.…”

Section: Introductionmentioning

confidence: 99%

Multi Robot Object Tracking and Self Localization Using Visual Percept Relations

Göhring

Burkhard

2006

2006 IEEE/RSJ International Conference on Intelligent Robots and Systems

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In this paper we present a novel approach to estimating the position of objects tracked by a team of mobile robots and to use these objects for a better self localization. Modeling of moving objects is commonly done in a robo-centric coordinate frame because this information is sufficient for most low level robot control and it is independent of the quality of the current robot localization. For multiple robots to cooperate and share information, though, they need to agree on a global, allocentric frame of reference. When transforming the egocentric object model into a global one, it inherits the localization error of the robot in addition to the error associated with the egocentric model.We propose using the relation of objects detected in camera images to other objects in the same camera image as a basis for estimating the position of the object in a global coordinate system. The spacial relation of objects with respect to stationary objects (e.g., landmarks) offers several advantages: a) Errors in feature detection are correlated and not assumed independent. Furthermore, the error of relative positions of objects within a single camera frame is comparably small. b) The information is independent of robot localization and odometry. c) As a consequence of the above, it provides a highly efficient method for communicating information about a tracked object and communication can be asynchronous. d) As the modeled object is independent from robo-centric coordinates, its position can be used for self localization of the observing robot.We present experimental evidence that shows how two robots are able to infer the position of an object within a global frame of reference, even though they are not localized themselves and then use this object information for self localization.

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“…The problem we address is to provide better localization for Nao [4] robots on a soccer field during an SPL game by merging their perceptions in a consistent way. Each robot is able to self-localize, and in our study we use a Monte Carlo Localization (MCL) algorithm [5] with a set of extensions as described in [6]. In MCL unique landmarks in the field are essential, however, there are no unique landmarks on the SPL field except the center circle.…”

Section: Introductionmentioning

confidence: 99%

Multi-robot Localization by Observation Merging

Erdem

Akın

2015

RoboCup 2014: Robot World Cup XVIII

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In robot soccer, self-localization of robots may fail because of perception failure, falling down or by being pushed by another robot. In this study, our goal is to improve self localization of robots using the teammate robots' perceptions. Robots which have perceived more landmarks and have moved less can share their localization and observations with other robots to improve localization accuracy. Currently, in the RoboCup Standard Platform League it is not feasible to identify the jersey number of robots using vision. Therefore, we merge perceptions of all robots depending on their reliability, and then identify them in a probabilistic manner to increase localization performance and provide a common world model that can be used for planning. We show that our approach has a significant advantage with respect to single robot localization on estimating the poses of the robots.

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Practical Extensions to Vision-Based Monte Carlo Localization Methods for Robot Soccer Domain

Cited by 7 publications

References 6 publications

Hidden Markov Model Based Visual Perception Filtering in Robotic Soccer

Hidden Markov Model Based Visual Perception Filtering in Robotic Soccer

Multi Robot Object Tracking and Self Localization Using Visual Percept Relations

Multi-robot Localization by Observation Merging

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