Bayesian Heuristics for Robust Spatial Perception

TL;DR

This paper introduces three Bayesian heuristics to improve robustness in spatial perception tasks like 3D registration and pose graph optimization, addressing outlier issues efficiently.

Contribution

The paper proposes three novel Bayesian heuristics that enhance robustness in nonlinear spatial perception estimation without requiring initial guesses.

Findings

Heuristics perform well in practical spatial perception scenarios.

Methods show computational advantages over traditional approaches.

Effective in applications like 3D point cloud and mesh registration.

Abstract

Spatial perception is a key task in several machine intelligence applications such as robotics and computer vision. In general, it involves the nonlinear estimation of hidden variables that represent the system's state. However, in the presence of measurement outliers, the standard nonlinear least squared formulation results in poor estimates. Several methods have been considered in the literature to improve the reliability of the estimation process. Most methods are based on heuristics since guaranteed global robust estimation is not generally practical due to high computational costs. Recently general purpose robust estimation heuristics have been proposed that leverage existing non-minimal solvers available for the outlier-free formulations without the need for an initial guess. In this work, we propose three Bayesian heuristics that have similar structures. We evaluate these heuristics in practical scenarios to demonstrate their merits in different applications including 3D point cloud registration, mesh registration and pose graph optimization. The general computational advantages our proposals offer make them attractive candidates for spatial perception tasks.