Risk-Aware Planning by Confidence Estimation using Deep Learning-Based Perception

Understand

This work proposes the use of Bayesian approximations of uncertainty from deep learning in a robot planner, showing that this produces more cautious actions in safety-critical scenarios.

• The case study investigated is motivated by a setup where an aerial robot acts as a "scout" for a ground robot.
• This is useful when the below area is unknown or dangerous, with applications in space exploration, military, or search-and-rescue.
• Images taken from the aerial view are used to provide a less obstructed map to guide the navigation of the robot on the ground.