Point Cloud-Based Control Barrier Function Regression for Safe and Efficient Vision-Based Control

Control barrier functions have become an increasingly popular framework for safe real-time control. In this work, we present a computationally low-cost framework for synthesizing barrier functions over point cloud data for safe vision-based control. We take advantage of surface geometry to locally define and synthesize a quadratic CBF over a point cloud. This CBF is used in a CBF-QP for control and verified in simulation on quadrotors and in hardware on quadrotors and the TurtleBot3. This technique enables safe navigation through unstructured and dynamically changing environments and is shown to be significantly more efficient than current methods.