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

Volume

1

fYear

1998

fDate

16-20 Aug 1998

Firstpage

482

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Pattern Recognition, 1998. Proceedings. Fourteenth International Conference on

Conference_Location

Brisbane, Qld.

ISSN

1051-4651

Print_ISBN

0-8186-8512-3

Type

conf

DOI

10.1109/ICPR.1998.711186

Filename

711186