ガウス過程による相関測定ノイズを考慮したカルマンフィルタ
Kalman Filtering with Gaussian Processes Measurement Noise
ロボティクスで時間相関のある測定ノイズをガウス過程で非パラメトリックにモデル化し、カルマンフィルタへ拡張してSLAMなどで精度向上を示した論文。
著者: Vince Kurtz, Hai Lin
分類: stat.ML, cs.LG, cs.RO, cs.SY, eess.SY
原文アブストラクト
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.
