- Tytuł:
- Computationally inexpensive appearance based terrain learning in unknown environments
- Autorzy:
-
Mishra, P.
Viswanathan, A. - Powiązania:
- https://bibliotekanauki.pl/articles/91850.pdf
- Data publikacji:
- 2013
- Wydawca:
- Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
- Tematy:
-
computationally inexpensive approach
learning
identification
autonomous navigation
Mixture of Gaussians
terrain model
pre-filtered pixels
terrain learning - Opis:
- This paper describes a computationally inexpensive approach to learning and identification of maneuverable terrain to aid autonomous navigation. We adopt a monocular vision based framework, using a single consumer grade camera as the primary sensor, and model the terrain as a Mixture of Gaussians. Self-supervised learning is used to identify navigable terrain in the perception space. Training data is obtained using pre-filtered pixels, which correspond to near-range traversable terrain. The scheme allows for on-line, and in-motion update of the terrain model. The pipeline architecture used in the proposed algorithm is made amenable to real-time implementation by restricting computations to bit-shifts and accumulate operations. Color based clustering using dominant terrain texture is then performed in perception sub-space. Model initialization and update follows at the coarse scale of an octave image pyramid, and is back projected onto the original fine scale. We present results of terrain learning, tested in heterogeneous environments, including urban road, suburban parks, and indoors. Our scheme provides orders of magnitude improvement in time complexity, when compared to existing approaches reported in literature.
- Źródło:
-
Journal of Artificial Intelligence and Soft Computing Research; 2013, 3, 3; 201-213
2083-2567
2449-6499 - Pojawia się w:
- Journal of Artificial Intelligence and Soft Computing Research
- Dostawca treści:
- Biblioteka Nauki
Artykuł