Vision-based Localization in RoboCup Environments

Enderle, Stefan; Ritter, Marcus; Fox, Dieter; Sablatnög, Stefan; Kraetzschmar, Gerhard K.; Palm, Günther

doi:10.1007/3-540-45324-5_28

Cited by 15 publications

(7 citation statements)

References 1 publication

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“…These features are fixed on the ground with regard to world coordinate, so the robot pose can be inferred through observations. 29,30 The rotation is described by Euler coordinates in this paper. The map used here is the standard field used in the competition, and the location of features relative to the map is known.…”

Section: Front-end Visual Feature Matchingmentioning

confidence: 99%

Self-Localization in Highly Dynamic Environments Based on Dual-Channel Unscented Particle Filter

2020

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SUMMARY Self-localization in highly dynamic environments is still a challenging problem for humanoid robots with limited computation resource. In this paper, we propose a dual-channel unscented particle filter (DC-UPF)-based localization method to address it. A key novelty of this approach is that it employs a dual-channel switch mechanism in measurement updating procedure of particle filter, solving for sparse vision feature in motion, and it leverages data from a camera, a walking odometer, and an inertial measurement unit. Extensive experiments with an NAO robot demonstrate that DC-UPF outperforms UPF and Monte–Carlo localization with regard to accuracy.

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Section: Front-end Visual Feature Matchingmentioning

confidence: 99%

Self-Localization in Highly Dynamic Environments Based on Dual-Channel Unscented Particle Filter

2020

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“…Instead of maintaining a belief for all poses, only a set of weighted samples is updated by equation 7 and 8. Since the method has been well documented by several other researchers [13,4], we will not discuss it in detail any further here. Instead, we will focus on the perceptual model and its integration to the sensor container.…”

Section: Plugin Based Localizationmentioning

confidence: 99%

A Plugin-Based Architecture for Simulation in the F2000 League

Kleiner

Buchheim

2004

RoboCup 2003: Robot Soccer World Cup VII

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Simulation has become an essential part in the development process of autonomous robotic systems. In the domain of robotics, developers often are confronted with problems like noisy sensor data, hardware malfunctions and scarce or temporarily inoperable hardware resources. A solution to most of the problems can be given by tools which allow the simulation of the application scenario in varying degrees of abstraction and the suppression of unwanted features of the domain (like e.g. sensor noise). The RoboCup scenario of autonomous mobile robots playing soccer is one such domain where the above mentioned problems typically arise. In this work we will present a flexible simulation platform for the RoboCup F2000 league developed as a joint open source project by the universities of Freiburg [14] and Stuttgart [8] which achieves a maximum degree of modularity by a plugin based architecture and allows teams to easily develop and share software modules for the simulation of different sensors, kinematics and even complete player behaviors. Moreover we show how plugins can be utilized to implement benchmark tasks for multi robot learning and give an example that demonstrates how the generic plugin approach can be extended towards the implementation of hardware independent algorithms for robot localization.

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“…As this is much easier than generating a position hypothesis through feature extraction and model matching, all algorithms based on raw distance data used Markov localization or more precise MCL, which became one of the most popular localization methods. Enderle et al [2] presented an approach using the distance to walls extracted from camera images, while Hundelshausen et al [12], Röfer et al [9,10] and Menegatti et al [8] used the distance to the field markings. These algorithms mainly differ in the efficiency of the assessment of position estimates and the number of samples needed for the localization.…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach to Efficient Monte-Carlo Localization in RoboCup

Heinemann

Haase

Zell

2007

RoboCup 2006: Robot Soccer World Cup X

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Recently, efficient self-localization methods have been developed, among which probabilistic Monte-Carlo localization (MCL) is one of the most popular. However, standard MCL algorithms need at least 100 samples to compute an acceptable position estimation. This paper presents a novel approach to MCL that uses an adaptive number of samples that drops down to a single sample if the pose estimation is sufficiently accurate. Experiments show that the method remains in this efficient single sample tracking mode for more than 90% of the cycles.

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Vision-based Localization in RoboCup Environments

Cited by 15 publications

References 1 publication

Self-Localization in Highly Dynamic Environments Based on Dual-Channel Unscented Particle Filter

Self-Localization in Highly Dynamic Environments Based on Dual-Channel Unscented Particle Filter

A Plugin-Based Architecture for Simulation in the F2000 League

A Novel Approach to Efficient Monte-Carlo Localization in RoboCup

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