State-based SHOSLIF for indoor visual navigation

Vision-based navigation is investigated using SHOSLIF that incorporates states and a visual attention mechanism. The problem is formulated as an observation-driven Markov model (ODMM) which is realized through recursive partitioning regression. A stochastic recursive partition tree (SRPT), which maps a preprocessed current input raw image and the previous state into the current state and the next control signal is used for efficient recursive partitioning regression. The SRPT learns incrementally: each learning sample is rejected or learned "on-the-fly". The proposed scheme has been successfully applied to indoor navigation.

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