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 CauchyUniform 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 GaussianUniform 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-ofthe-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 1.