Lifelong 3D Mapping Framework for Hand-held & Robot-mounted LiDAR Mapping Systems
Liudi Yang, Sai Manoj Prakhya, Senhua Zhu, Ziyuan Liu

TL;DR
This paper introduces a modular, cloud-native lifelong 3D mapping framework compatible with hand-held and robot-mounted LiDAR systems, featuring dynamic point removal, automatic multi-session alignment, change detection, and version control.
Contribution
The framework's novelty lies in its sensor-agnostic design, automated multi-session map alignment, and a unique map version control system that efficiently manages environment changes.
Findings
Effective dynamic point removal for clean static maps
Automatic multi-session map alignment without manual tuning
Accurate change detection and efficient map version management
Abstract
We propose a lifelong 3D mapping framework that is modular, cloud-native by design and more importantly, works for both hand-held and robot-mounted 3D LiDAR mapping systems. Our proposed framework comprises of dynamic point removal, multi-session map alignment, map change detection and map version control. First, our sensor-setup agnostic dynamic point removal algorithm works seamlessly with both hand-held and robot-mounted setups to produce clean static 3D maps. Second, the multi-session map alignment aligns these clean static maps automatically, without manual parameter fine-tuning, into a single reference frame, using a two stage approach based on feature descriptor matching and fine registration. Third, our novel map change detection identifies positive and negative changes between two aligned maps. Finally, the map version control maintains a single base map that represents the…
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Taxonomy
MethodsBalanced Selection
