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Uncertainty-aware Perception Models for Off-road Autonomous Unmanned Ground Vehicles. (arXiv:2209.11115v1 [cs.RO])
Sept. 23, 2022, 1:11 a.m. | Zhaoyuan Yang, Yewteck Tan, Shiraj Sen, Johan Reimann, John Karigiannis, Mohammed Yousefhussien, Nurali Virani
cs.LG updates on arXiv.org arxiv.org
Off-road autonomous unmanned ground vehicles (UGVs) are being developed for
military and commercial use to deliver crucial supplies in remote locations,
help with mapping and surveillance, and to assist war-fighters in contested
environments. Due to complexity of the off-road environments and variability in
terrain, lighting conditions, diurnal and seasonal changes, the models used to
perceive the environment must handle a lot of input variability. Current
datasets used to train perception models for off-road autonomous navigation
lack of diversity in seasons, …
More from arxiv.org / cs.LG updates on arXiv.org
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