ノイズ付き計測のみからのパラメータ学習を伴う変分推論による車両軌道推定
Variational Inference with Parameter Learning Applied to Vehicle Trajectory Estimation
ESGVIの枠組みにモデルパラメータの学習を組み込み、ノイズ付き計測のみから車両の運動・センサモデルの共分散を学習して高精度な軌道推定を実現した。
著者: Jeremy N. Wong, David J. Yoon, Angela P. Schoellig, Timothy D. Barfoot
分類: cs.RO, stat.ML
原文アブストラクト
We present parameter learning in a Gaussian variational inference setting using only noisy measurements (i.e., no groundtruth). This is demonstrated in the context of vehicle trajectory estimation, although the method we propose is general. The paper extends the Exactly Sparse Gaussian Variational Inference (ESGVI) framework, which has previously been used for large-scale nonlinear batch state estimation. Our contribution is to additionally learn parameters of our system models (which may be difficult to choose in practice) within the ESGVI framework. In this paper, we learn the covariances for the motion and sensor models used within vehicle trajectory estimation. Specifically, we learn the parameters of a white-noise-on-acceleration motion model and the parameters of an Inverse-Wishart prior over measurement covariances for our sensor model. We demonstrate our technique using a 36~km dataset consisting of a car using lidar to localize against a high-definition map; we learn the parameters on a training section of the data and then show that we achieve high-quality state estimates on a test section, even in the presence of outliers. Lastly, we show that our framework can be used to solve pose graph optimization even with many false loop closures.
