Variational Inference with Parameter Learning Applied to Vehicle Trajectory Estimation
This work addresses the challenge of parameter selection in vehicle trajectory estimation for autonomous driving, though it is incremental as it extends an existing framework.
The paper tackles the problem of learning system model parameters, such as motion and sensor covariances, within a Gaussian variational inference framework for vehicle trajectory estimation, achieving high-quality state estimates on a 36 km test dataset even with outliers and false loop closures.
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.