Global Localization in Meshes

Abstract

Safely waking up a robot at an unknown location and subsequent autonomous operation are key requirements for on-site construction robots. In this regard, single-shot global localization in a known map is a challenging problem due to incomplete observations of the environment and sensor obstructions by unmapped clutter. In this work, we address global localization of sparse multi-beam LiDAR measurements in a 3D mesh building model, a typical setup for construction robots. Our solution extracts and summarizes planes from the LiDAR scan and matches them to the building mesh. We evaluate different options for the registration problem, and evaluate the system on simulated and real-world datasets. The best performing system uses a combination of the Randomized Hough Transform (RHT) and a modified version of the Plane Registration based on a Unit Sphere (PRRUS) algorithm. For sparse and noisy robotic sensors, our system outperforms contemporary systems like Go-ICP by a large margin.