Towards evasive maneuvers with quadrotors using dynamic vision sensors

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.