State-based SHOSLIF for indoor visual navigation

In this paper, we investigate vision-based navigation using the self-organizing hierarchical optimal subspace learning and inference framework (SHOSLIF) that incorporates states and a visual attention mechanism. With states to keep the history information and regarding the incoming video input as an observation vector, the vision-based navigation is formulated as an observation-driven Markov model (ODMM). The ODMM can be realized through recursive partitioning regression. A stochastic recursive partition tree (SRPT), which maps an 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 learned or rejected "on-the-fly." The purposed scheme has been successfully applied to indoor navigation.