Kalman Filtering with Gaussian Processes Measurement Noise
This addresses noise modeling for sensor systems and perception algorithms, but appears incremental as it extends existing results to more general Gaussian Processes.
The paper tackles the problem of correlated measurement noise in filtering applications like robotics by proposing Gaussian Processes as a non-parametric model, and demonstrates improved performance in Kalman filtering.
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.