1 research outputs found
Uncertainty-aware Perception Models for Off-road Autonomous Unmanned Ground Vehicles
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, locations, semantic classes, as well as time of
day. We test the hypothesis that model trained on a single dataset may not
generalize to other off-road navigation datasets and new locations due to the
input distribution drift. Additionally, we investigate how to combine multiple
datasets to train a semantic segmentation-based environment perception model
and we show that training the model to capture uncertainty could improve the
model performance by a significant margin. We extend the Masksembles approach
for uncertainty quantification to the semantic segmentation task and compare it
with Monte Carlo Dropout and standard baselines. Finally, we test the approach
against data collected from a UGV platform in a new testing environment. We
show that the developed perception model with uncertainty quantification can be
feasibly deployed on an UGV to support online perception and navigation tasks