Robust Point Cloud Based Reconstruction of Large-Scale Outdoor Scenes
This addresses robust reconstruction for outdoor scenes, but is incremental as it builds on existing methods with specific improvements.
The paper tackles the problem of outlier feature matches and loop-closures causing failures in large-scale point cloud 3D reconstruction by proposing a probabilistic Bayesian network approach using Expectation-Maximization, achieving comparable performance on an indoor benchmark and outperforming state-of-the-art on a large-scale outdoor dataset.
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.