This paper presents a probabilistic algorithm to collaborate distributed sensors for mobile robot localization. It uses a sample-based version of Markov localization—Monte Carlo localization (MCL), capable of localizing mobile robot in an any-time fashion. During robot localization given a known environment model, MCL method is employed to update robot’s belief whichever information (positive or negative) attained from environmental sensors. Meanwhile, an implementation is presented that uses color environmental cameras for robot detection. 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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