State-Based SHOSLIF for Indoor Visual Navigation  

Oleh:

Weng, Juyang ; Chen, Shaoyun

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 11 no. 6 (2000), page 1300-1314.
Topik: navigation; SHOSLIF; indoor visual navigation

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.4 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

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.

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