Sense and avoid based on visual pose estimation for small UAS

Abstract

Small unmanned aircraft systems (UAS) must be able to detect and avoid conflicting traffic, an especially challenging task when the threat is another small UAS. Collision avoidance requires trajectory prediction and the performance of a collision avoidance system can be improved by extending the prediction horizon. We describe an algorithm that predicts the trajectory of a small, fixed-wing UAS using an estimate of its orientation. First, a computer vision algorithm locates specific feature points of the threat aircraft in an image. Next, the POSIT algorithm uses these feature points to estimate the pose (position and attitude) of the threat. A sequence of pose estimates is then used to predict the trajectory of the threat aircraft in order to avoid a collision. To assess the algorithm's performance, the predictions are compared with predictions based solely on position estimates for a variety of encounter scenarios. Simulation and experimental results indicate that trajectory prediction using orientation estimates provides quicker response to a change in the threat aircraft trajectory and better prediction and avoidance performance.