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Off-Road Drivable Area Extraction Using 3D LiDAR Data

2020-03-10 14:44:45
Biao Gao, Anran Xu, Yancheng Pan, Xijun Zhao, Wen Yao, Huijing Zhao
arXiv_CV
             
Abstract

We propose a method for off-road drivable area extraction using 3D LiDAR data with the goal of autonomous driving application. A specific deep learning framework is designed to deal with the ambiguous area, which is one of the main challenges in the off-road environment. To reduce the considerable demand for human-annotated data for network training, we utilize the information from vast quantities of vehicle paths and auto-generated obstacle labels. Using these autogenerated annotations, the proposed network can be trained using weakly supervised or semi-supervised methods, which can achieve better performance with fewer human annotations. The experiments on our dataset illustrate the reasonability of our framework and the validity of our weakly and semi-supervised methods.

Abstract (translated)

URL

https://arxiv.org/abs/2003.04780

PDF

https://arxiv.org/pdf/2003.04780.pdf

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