Mapping of Incremental Dynamic Environment Using Rao-Blackwellized Particle Filter
This research is a preliminary research for real time autonomous robot in unknown incremental dynamical environment. A general method for mapping incremental dynamic environment using Multiple Target tracking (MTT) was proposed in this research. Rao-Blackwellized Particle Filter (RBPF) was used for the multiple moving obstacles tracking problem. Firstly data association problem was solved via Multiple Hypothesis Tracking (MHT) data association by a new method. The new MHT method can use extra information except using only position of targets. Particle Filter is used in the method. Each particle is assumed as an obstacle map. Then tracking problem for each obstacle in the particle is solved by Extended Kalman Filter (EKF). Finally the particle which has highest weight is assumed as the dynamic map. Additionally a new resampling method was proposed in this research. The algorithm can cope with new obstacles and false detection according to the pure particle filter. Obstacles are assumed as human in this research hence their velocities are determined randomly up to human walking speed. Furthermore the robot moves approximately at human walking speed. A graphical user interface program was constituted in MATLAB so different states are surveyed.
KeywordsRao-Blackwellized Particle Filter Multiple Hypothesis Tracking Data Association Dynamic Obstacle Detection in Unknown Environment Mapping
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