A Novel Approach to Efficient Monte-Carlo Localization in RoboCup

Heinemann, Patrick; Haase, Jürgen; Zell, Andreas

doi:10.1007/978-3-540-74024-7_29

Cited by 8 publications

(12 citation statements)

References 9 publications

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“…Second, the extraction of the green and the white color class is robust enough to cope with a sudden change in illumination as shown in section II-A. This enables the robust self-localization [5] to keep track of the correct pose of the robot until the other color classes are adapted.…”

Section: The Automatic Color Training Algorithmmentioning

confidence: 99%

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An Automatic Approach to Online Color Training in RoboCup Environments

Heinemann

Sehnke

Streichert

et al. 2006

2006 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-Many approaches for extracting landmarks and objects from a camera image based on their color coding were published in the RoboCup domain. They are quite sophisticated and tuned to the typical RoboCup scenario of constant bright lighting using a static subdivision of the color space into different color classes. However, such algorithms will soon be of limited use, as the future requirements of RoboCup include the possibility to play under changing and finally natural lighting. This paper presents an algorithm for automatic online color training, which is able to robustly adapt the mapping of colors to color classes onto different lighting situations online. Using the ACT algorithm a robot will be able to play a RoboCup match while the illumination of the field varies.

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Section: The Automatic Color Training Algorithmmentioning

confidence: 99%

“…By incorporating the pose of the robot computed by our self-localization algorithm ( [5]) this algorithm is able to constantly re-train the mapping according to the changing lighting conditions. By keeping the amount of training per cycle as low as possible, the algorithm can be processed 50 times a second on our RoboCup MSL robots.…”

Section: Introductionmentioning

confidence: 99%

An Automatic Approach to Online Color Training in RoboCup Environments

Heinemann

Sehnke

Streichert

et al. 2006

2006 IEEE/RSJ International Conference on Intelligent Robots and Systems

Self Cite

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“…One of the most used algorithms for self-localization is Monte Carlo [8] [1] [5]. Since Monte Carlo is very used, some improvements on it had been proposed [3] [7]. However, rather than proposing a new localization algorithm, this paper proposes a new way of sorting out one step of the localization task, namely the orientation.…”

Section: Introductionmentioning

confidence: 99%

Robot Orientation with Histograms on MSL

Ribeiro

Lopes

Pereira

et al. 2012

Lecture Notes in Computer Science

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One of the most important tasks on robot soccer is localization. The team robots should self-localize on the 18 x 12 meters soccer field. Since a few years ago the soccer field has increased and the corner posts were removed and that increased the localization task complexity. One important aspect to take care for a proper localization is to find out the robot orientation. This paper proposes a new technique to calculate the robot orientation. The proposed method consists of using a histogram of white-green transitions (to detect the lines on the field) to know the robot orientation. This technique does not take much computational time and proves to be very reliable.

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“…A common method to determine the number of particles is KLD-sampling [12]. Heinemann et al project the current sensor data onto the map, based on the position estimate [13]. They choose the number of particles based on the distance between the map data and the projected sensor data.…”

Section: Introductionmentioning

confidence: 99%

A hybrid approach for vision-based outdoor robot localization using global and local image features

Weiß

Tamimi²,

Masselli

et al. 2007

2007 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-Vision-based robot localization in outdoor environments is difficult because of changing illumination conditions. Another problem is the rough and cluttered environment which makes it hard to use visual features that are not rotation invariant. A popular method that is rotation invariant and relatively robust to changing illumination is the Scale Invariant Feature Transform (SIFT). However, due to the computationally intensive feature extraction and image matching, localization using SIFT is slow. On the other hand, techniques which use global image features are in general less robust and exact than SIFT, but are often much faster due to fast image matching. In this paper, we present a hybrid localization approach that switches between local and global image features. For most images, the hybrid approach uses fast global features. Only in difficult situations, e.g. containing strong illumination changes, the hybrid approach switches to local features. To decide which features to use for an image, we analyze the particle cloud of the particle filter that we use for position estimation. Experiments on outdoor images taken under varying illumination conditions show that the position estimates of the hybrid approach are about as exact as the estimates of SIFT alone. However, the average localization time using the hybrid approach is more than 3.5 times faster than using SIFT.

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A Novel Approach to Efficient Monte-Carlo Localization in RoboCup

Cited by 8 publications

References 9 publications

An Automatic Approach to Online Color Training in RoboCup Environments

An Automatic Approach to Online Color Training in RoboCup Environments

Robot Orientation with Histograms on MSL

A hybrid approach for vision-based outdoor robot localization using global and local image features

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