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 state-of-the-art approach for
robust indoor reconstruction. Experimental results demonstrate that our
approach has comparable performance with the state-of-the-art on a benchmark
indoor dataset, and outperforms it on a large-scale outdoor dataset. Our source
code can be found on the project website.

本文提出一种基于概率方法的鲁棒后端优化方案，通过建立贝叶斯网络模型，利用长尾柯西分布抑制异常点的特征匹配，以及一组二元潜在变量的柯西 - 均匀混合模型同时抑制循环闭合约束中的异常点特征匹配。并在实验中证明了我们的方法在大规模室内和室外数据集上均具有良好的性能表现。