Information-theoretic Approaches Based on Sequential Monte Carlo to Collaborative Distributed Sensors for Mobile Robot Localization

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We consider the Sequential Monte Carlo (SMC) method for Bayesian inference applied to the problem of information-theoretic distributed sensor collaboration in complex environments. The robot kinematics and sensor observation under consideration are described by nonlinear models. The exact solution to this problem is prohibitively complex due to the nonlinear nature of the system. The SMC method is, therefore, employed to track the probabilistic kinematics of the robot and to make the corresponding Bayesian estimates and predictions. To meet the specific requirements inherent in distributed sensors, such as low-communication consumption and collaborative information processing, we propose a novel SMC solution that makes use of the particle filter technique for data fusion, and the density tree representation of the a posterior distribution for information exchange between sensor nodes. Meanwhile, an efficient numerical method is proposed for approximating the information utility in sensor selection. A further experiment, obtained with a real robot in an indoor environment, illustrates that under the SMC framework, the optimal sensor selection and collaboration can be implemented naturally, and significant improvement in localization accuracy is achieved when compared to conventional methods using all sensors.

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