Incremental-Segment-Based Localization in 3-D Point Clouds

Localization in 3-D point clouds is a highly challenging task due to the complexity associated with extracting information from 3-D data. This letter proposes an incremental approach addressing this problem efficiently. The presented method first accumulates the measurements in a dynamic voxel grid and selectively updates the point normals affected by the insertion. An incremental segmentation algorithm, based on region growing, tracks the evolution of single segments, which enables an efficient recognition strategy using partitioning and caching of geometric consistencies. We show that the incremental method can perform global localization at 10 Hz in an urban driving environment, a speedup of $\times$7.1 over the compared batch solution. The efficiency of the method makes it suitable for applications where real-time localization is required and enables its usage on cheaper low-energy systems. Our implementation is available open source along with instructions for running the system. (The implementation is available at https://github.com/ethz-asl/segmatch and a video demonstration is available at https://youtu.be/cHfs3HLzc2Y .)