  Real-world measurement noise in applications like robotics is often
correlated in time, but we typically assume i.i.d. Gaussian noise for
filtering. We propose general Gaussian Processes as a non-parametric model for
correlated measurement noise that is flexible enough to accurately reflect
correlation in time, 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.
