  The broad scope of obstacle avoidance has led to many kinds of computer
vision-based approaches. Despite its popularity, it is not a solved problem.
Traditional computer vision techniques using cameras and depth sensors often
focus on static scenes, or rely on priors about the obstacles. Recent
developments in bio-inspired sensors present event cameras as a compelling
choice for dynamic scenes. Although these sensors have many advantages over
their frame-based counterparts, such as high dynamic range and temporal
resolution, event-based perception has largely remained in 2D. This often leads
to solutions reliant on heuristics and specific to a particular task. We show
that the fusion of events and depth overcomes the failure cases of each
individual modality when performing obstacle avoidance. Our proposed approach
unifies event camera and lidar streams to estimate metric time-to-impact
without prior knowledge of the scene geometry or obstacles. In addition, we
release an extensive event-based dataset with six visual streams spanning over
700 scanned scenes.
