Research Article

Improved Monte Carlo Localization Algorithm in a Hybrid Robot and Camera Network

Zhiwei Liang, Xudong Ma, Fang Fang and Songhao Zhu

In order to overcome the difficulty of a mobile robot to perform localization only with its onboard sensors, this study presents a probabilistic algorithm Monte Carlo Localization (MCL) to solve the problem of mobile robot localization in a hybrid robot and camera network in real time. On one hand, the robot does perform localization depending on its laser sensor using Monte Carlo method. On the other hand, environment cameras can detect the robot in their field of view during robot localization. According to a built environment camera model, MCL method extended to update robot’s belief whichever information (positive or negative) attained from environmental camera sensors. Meanwhile, all the parameters of each environmental camera are unknown in advance and need be calibrated independently by robot. Once calibrated, the positive and negative detection models can be built up according to the parameters of environmental cameras. A further experiment, obtained with the real robot in an indoor office environment, illustrates it has drastic improvement in global localization speed and accuracy using our algorithm.

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Zhiwei Liang, Xudong Ma, Fang Fang and Songhao Zhu, 2010. Improved Monte Carlo Localization Algorithm in a Hybrid Robot and Camera Network. Information Technology Journal, 9: 1585-1597.

DOI: 10.3923/itj.2010.1585.1597

URL: https://scialert.net/abstract/?doi=itj.2010.1585.1597

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Authors

Zhiwei Liang

Xudong Ma

Fang Fang

Songhao Zhu

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Research Article

Improved Monte Carlo Localization Algorithm in a Hybrid Robot and Camera Network

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