Title :
State-based SHOSLIF for indoor visual navigation
Author :
Chen, Shaoyun ; Weng, John
Author_Institution :
Dept. of Comput. Sci., Michigan State Univ., East Lansing, MI, USA
Abstract :
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
Keywords :
Markov processes; learning (artificial intelligence); mobile robots; neural nets; path planning; robot vision; statistical analysis; incremental learning; indoor visual navigation; observation-driven Markov model; recursive partitioning regression; state-based SHOSLIF; stochastic recursive partition tree; vision-based navigation; visual attention mechanism; Artificial neural networks; Change detection algorithms; Clustering algorithms; Computer science; Ear; Image edge detection; Mobile robots; Navigation; Neural networks; Roads;
Conference_Titel :
Pattern Recognition, 1998. Proceedings. Fourteenth International Conference on
Conference_Location :
Brisbane, Qld.
Print_ISBN :
0-8186-8512-3
DOI :
10.1109/ICPR.1998.711186
