Variational Filtering with Copula Models for SLAM
This addresses the challenge of accurate SLAM for autonomous mobile robots in non-Gaussian environments, representing an incremental improvement by extending existing methods with copula models.
The paper tackles the problem of simultaneous localization and mapping (SLAM) by relaxing the unrealistic Gaussian assumption for variable dependencies, using copula models to represent a larger class of distributions. It integrates this with a Sequential Monte Carlo estimator and gradient-based optimization, demonstrating effectiveness in non-Gaussian settings like uncertain data association and nonlinear transitions.
The ability to infer map variables and estimate pose is crucial to the operation of autonomous mobile robots. In most cases the shared dependency between these variables is modeled through a multivariate Gaussian distribution, but there are many situations where that assumption is unrealistic. Our paper shows how it is possible to relax this assumption and perform simultaneous localization and mapping (SLAM) with a larger class of distributions, whose multivariate dependency is represented with a copula model. We integrate the distribution model with copulas into a Sequential Monte Carlo estimator and show how unknown model parameters can be learned through gradient-based optimization. We demonstrate our approach is effective in settings where Gaussian assumptions are clearly violated, such as environments with uncertain data association and nonlinear transition models.