Data-fusion for robust off-road perception considering data quality of uncertain sensors

Robust off-road perception for autonomous navigation is hard to achieve. Versatile environments, different hardware, and numerous disturbances limit the perceptional portability in changing applications and cross-platform. This contribution proposes sensor-fusion considering the data quality of uncertain sensors to increase the classification and mapping components’ perceptual robustness. The resulting benefits on perception are demonstrated using the autonomous off-road robot U5023.