Towards evasive maneuvers with quadrotors using dynamic vision sensors

Abstract

We present a method to predict collisions with objects thrown at a quadrotor using a pair of dynamic vision sensors (DVS). Due to the micro-second temporal resolution of these sensors and the sparsity of their output, the object's trajectory can be estimated with minimal latency. Unlike standard cameras that send frames at a fixed frame rate, a DVS only transmits pixel-level brightness changes (“events”) at the time they occur. Our method tracks spherical objects on the image plane using probabilistic trackers that are updated with each incoming event. The object's trajectory is estimated using an Extended Kalman Filter with a mixed state space that allows incorporation of both the object's dynamics and the measurement noise in the image plane. Using error-propagation techniques, we predict a collision if the 3σ-ellipsoid along the predicted trajectory intersects with a safety sphere around the quadrotor. We experimentally demonstrate that our method allows initiating evasive maneuvers early enough to avoid collisions.