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Bayesian Heuristics for Robust Spatial Perception
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DOI：10.1109/TIM.2024.3364269.png)
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Abstract
En 中文
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 nonminimal 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 3-D point cloud registration, mesh registration, and pose graph optimization. The general computational advantages our proposals offer make them attractive candidates for spatial perception tasks.
Keywords:
Estimation
Bayes methods
Standards
Task analysis
Proposals
Reliability
Noise measurement
Expectation-maximization
measurement outliers
nonlinear estimation
parameter and state estimation
spatial perception
statistical inference
variational Bayes (VB)
Journal
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5.9
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2.0W
Citations:
5.8W
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