Robust Point Cloud Based Reconstruction of Large-Scale Outdoor Scenes
Ziquan Lan, Zi Jian Yew, Gim Hee Lee

TL;DR
This paper introduces a probabilistic Bayesian network approach using mixture models and EM algorithm to improve robustness in large-scale outdoor 3D scene reconstruction by effectively handling outliers in point cloud data.
Contribution
It proposes a novel Bayesian network model with mixture distributions and EM optimization for robust large-scale outdoor scene reconstruction, outperforming existing methods.
Findings
Outperforms state-of-the-art on outdoor datasets
Comparable performance with indoor methods on benchmarks
Effectively suppresses outliers in large-scale reconstructions
Abstract
Outlier feature matches and loop-closures that survived front-end data association can lead to catastrophic failures in the back-end optimization of large-scale point cloud based 3D reconstruction. To alleviate this problem, we propose a probabilistic approach for robust back-end optimization in the presence of outliers. More specifically, we model the problem as a Bayesian network and solve it using the Expectation-Maximization algorithm. Our approach leverages on a long-tail Cauchy distribution to suppress outlier feature matches in the odometry constraints, and a Cauchy-Uniform mixture model with a set of binary latent variables to simultaneously suppress outlier loop-closure constraints and outlier feature matches in the inlier loop-closure constraints. Furthermore, we show that by using a Gaussian-Uniform mixture model, our approach degenerates to the formulation of a…
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Taxonomy
TopicsRobotics and Sensor-Based Localization · 3D Surveying and Cultural Heritage · Remote Sensing and LiDAR Applications
