Probabilistic Correspondence in Video Sequences for Efficient State Estimation and Autonomous Flight

In this paper, we describe a method for using Bayesian inference to create probabilistic point correspondences in video sequences. Using state prior distribution information from the onboard Kalman filter found in most unmanned aerial vehicles, we derive an accurate normal approximation for the movement of points in image space. It is then a direct application of Bayesian inference to find the probability that a feature point found in the current image is the same feature point found in a previous image. The biggest advantage of this method is that it does not require the use of feature descriptors, which can be computationally expensive to compute. The algorithm is tested in two scenarios. The first uses data and images collected from a sample flight to evaluate velocity estimation performance, which is critical for achieving good control. When compared to using a standard matching algorithm based on ORB (oriented FAST and rotate BRIEF) descriptors, our algorithm provides both improved efficiency and accuracy. We then implement the algorithm for online testing as well as develop a simple visual odometry method based on probabilistic correspondences for full state estimation. Flight tests demonstrate fully autonomous indoor hover using only the sensors embedded in the onboard smartphone and sonar.