This paper focuses on terrain classification in field environment and proposes a self-supervised terrain classification method which is based on 3D laser sensor and monocular vision sensor to adapt to changes in terrain environment and external conditions. First of all, extract typical traversable areas and typical obstacle areas by analyzing range data from 3D laser sensor and project these two kinds of areas into image space to label the image data. Then extract visual feature from the corresponding image to train classifier to classify the terrain. The experiment results demonstrate that the proposed method in this paper can obtain high classification accuracy and good real-time performance.
Adaptive terrain classification in field environment based on self-supervised learning
01.08.2014
240399 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Supervised Terrain Classification with Adaptive Unsupervised Terrain Assessment
British Library Conference Proceedings | 2021
|Supervised Terrain Classification with Adaptive Unsupervised Terrain Assessment
SAE Technical Papers | 2021
|Self-supervised terrain classification for planetary surface exploration rovers
British Library Online Contents | 2012
|British Library Online Contents | 2012
|