Kalman Filtering with Gaussian Processes Measurement Noise

--Real world measurement noise in applications like robotics is often correlated in time, but we typically assume i.i.d. We propose general Gaussian Processes as a nonparametric model for correlated measurement noise that is flexible enough to accurately reflect time-correlated measurement noise, yet simple enough to enable efficient computation. We show that this model accurately reflects the measurement noise resulting from vision-based Simultaneous Localization and Mapping (SLAM), and argue that it provides a flexible means of modeling measurement noise for a wide variety of sensor systems and perception algorithms. We then extend existing results for Kalman filtering with autoregressive processes to more general Gaussian Processes, and demonstrate the improved performance of our approach. A. Motivation Robotic systems often rely on advanced perception algorithms and complex sensor suites.