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• W2971265822 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 auto-generated 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." @default.
• W2971265822 created "2019-09-05" @default.
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• W2971265822 date "2019-06-01" @default.
• W2971265822 modified "2023-10-16" @default.
• W2971265822 title "Off-Road Drivable Area Extraction Using 3D LiDAR Data" @default.
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• W2971265822 doi "https://doi.org/10.1109/ivs.2019.8814143" @default.
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