Please use this identifier to cite or link to this item:
|Title:||Off-road obstacle detection with robust parametric modeling of the ground stereo geometry||Authors:||Kodagoda, S.
Piecewise linear approximation
|Issue Date:||2009||Citation:||Kodagoda, S.,Dong, G.,Yan, C.H.,Ong, S.H. (2009). Off-road obstacle detection with robust parametric modeling of the ground stereo geometry. Proceedings of the IASTED International Conference on Robotics and Applications : 343-350. ScholarBank@NUS Repository.||Abstract:||Autonomous navigation in off-road environments presents many challenges in contrast to the more conventional, urban environments. Unstructured surroundings, non-uniform visual cues and lack of prior knowledge about the scene complicate the design of even basic functionalities such as obstacle detection. This paper presents a stereo vision based ground geometry modeling and obstacle detection algorithm that is well suited for cross-country navigation. Our mathematical analysis shows that the "ν-disparity" method is inadequate for accurate terrain modeling under vehicle pose variations; to compensate for this shortcoming, we propose a novel extension to the original algorithm. As the preliminary step of this extension, lateral gradient of the ground disparity is estimated using histogram analysis. This information is subsequently propagated to a modified "ν-disparity" algorithm that models the longitudinal terrain disparity variation. The effectiveness of this two-phase ground modeling technique for obstacle detection is demonstrated with empirical results.||Source Title:||Proceedings of the IASTED International Conference on Robotics and Applications||URI:||http://scholarbank.nus.edu.sg/handle/10635/71198||ISBN:||9780889868137||ISSN:||1027264X|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Jun 28, 2020
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.