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 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 learned or rejected "on-the-fly." The proposed scheme has been successfully applied to indoor navigation.