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A Hidden Markov Model based efficient next best viewpoint planning for autonomous exploration

A next best viewpoint planning algorithm based on Hidden Markov Model (HMM) was presented for autonomous exploration. The grid map and probabilistic assessment model based on HMM was established to evaluate the next best exploration perspective. Leveraging the relative information entropy, the maximum information gain of candidate viewpoints were assessed. After the establishment of the actual scene graph rule, the state transfer matrix and observation occupied state transition matrix were derived. Used Bayesian filtering to update the probabilistic grid diagram, the path planning was selected from starting point to the maximum information return point. By simulation experiment scene of different complexity, the efficiency and its good robustness was confirmed.