Semantic Iterative Closest Point through Expectation-Maximization

Summary

Steven A. Parkison, Lu Gan, Maani Ghaffari Jadidi and Ryan M. Eustice, Semantic Iterative Closest Point through Expectation-Maximization. In Proceedings of the British Machine Vision Conference, pages 1-17, Newcastle, UK, September 2018.

Abstract

In this paper, we develop a novel point cloud registration algorithm that directly incorporates pixelated semantic measurements into the estimation of the relative transformation between two point clouds. The algorithm uses an Iterative Closest Point (ICP)-like scheme and performs joint semantic and geometric inference using the Expectation-Maximization technique in which semantic labels and point associations between two point clouds are treated as latent random variables. The minimization of the expected cost on the three-dimensional special Euclidean group, i.e., SE(3), yields the rigid body transformation between two point clouds. The evaluation on publicly available RGBD benchmarks shows that, in comparison with both the standard Generalized ICP (GICP) available in the Point Cloud Library and GICP on SE(3), the registration error is reduced.

Bibtex entry