Detail Visual Learning With Navigation as An Example   Oleh:  Weng, J. ; Chen, S. Jenis: Article from Bulletin/Magazine Dalam koleksi: IEEE Intelligent Systems vol. 15 no. 5 (2000), page 63-71. Topik: navigation; visual learning; navigation Ketersediaan Perpustakaan Pusat (Semanggi) Nomor Panggil: II60.4A Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada     Lihat Detail Induk Isi artikel The state - based learning method presented is applicable to virtually any vision - based control problem. We use navigation as an example. In a controlled environment, we can define a few known landmarks before system design, and the navigation system can employ landmark detectors. Such navigation systems typically employ a model-based design method. However these methods have difficulties dealing with learning in complex, changing environments. To overcome these limitations, we have developed Shoslif (Self - organizing Hierarchical Optimal Subspace Learning and Inference Framework), a model - free, learning - based approach. Shoslif introduces mechanisms such as automatic feature derivation, a self - organizing tree structure to reach a very low logarithmic time complexity, one -instance learning, and incremental learning without forgetting prior memorized information. In addition, we have created a state - based version of Shoslif that lets humans teach robots to use past history and local views that are useful for disambiguation. Shoslif - N is a prototype autonomous navigation system using Shoslif. We have tested Shoslif - N primarily indoors. Indoor navigation encounters fewer lighting changes than outdoor navigation. However, it offers other, considerable challenges for vision -based navigation. Shoslif - N has shown that it can navigate in real time reliably in an unaltered indoor environment for an extended amount of time and distance, without any special image - processing hardware. Opini Anda Klik untuk menuliskan opini Anda tentang koleksi ini! Kembali

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