IRON: A fast interest point descriptor for robust NDT-map matching and its application to robot localization

摘要

This work introduces the IRON keypoint detector and the IRON descriptor which enable high-speed and high-accuracy alignment of 3D depth maps. Instead of using raw point values for storing 3D-scenes, all algorithms were designed to operate on Normal Distribution Transforms (NDT), since NDT-maps provide a highly memory-efficient representation of depth data. By taking into account surface curvature and object shape within NDT-maps, patches with strong surface variability can be recognized and described precisely. In this paper, the whole feature extraction process, as well as descriptor matching, outlier detection, and the final transform calculation between NDT-maps is elaborated. The presented technique is particularly insensitive to an initial offset between both maps, has a high robustness, and it achieves more than 75 NDT-map alignments per second (including complete memory allocation each time as well) in two large publicly available depth datasets while using only a single core of a modern Intel i7 CPU. Even though the main focus of this work was placed on the proposed IRON registration algorithm, two specific applications of this NDT-matching approach are outlined in the second part, namely robot pose tracking and NDT-one-shot localization within densely furnished domestic environments.