Lifelong 3D Mapping Framework for Hand-Held & Robot-Mounted LiDAR Mapping Systems
🤖 AI Summary
Existing lifelong 3D mapping systems in dynamic environments suffer from inefficient map updates, lack of version management, and poor cross-session consistency. Method: We propose the first cloud-native lifelong mapping framework for handheld and robotic LiDARs, integrating dynamic point removal, feature-based coarse-to-fine multi-session alignment, voxel-wise differential change detection, and a novel lightweight incremental map versioning system—storing only change deltas and boundary metadata to enable lossless reconstruction of any historical map and bidirectional change queries without retaining raw point clouds. Contribution/Results: Evaluated on commercial handheld LiDARs and open-source robotic SLAM platforms, our fully automated system achieves 72% memory reduction, 94.3% change detection accuracy, and enables parameter-free, cross-device, high-fidelity continuous environmental modeling.
Application Category
📝 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 current state of the environment, and stores the detected positive and negative changes, and boundary information. Our unique map version control system can reconstruct any of the previous clean session maps and allows users to query changes between any two random mapping sessions, all without storing any input raw session maps, making it very unique. Extensive experiments are performed using hand-held commercial LiDAR mapping devices and open-source robot-mounted LiDAR SLAM algorithms to evaluate each module and the whole 3D lifelong mapping framework.
Problem
Research questions and friction points this paper is trying to address.
3D Mapping
LiDAR Systems
Lifetime Map Maintenance
Innovation
Methods, ideas, or system contributions that make the work stand out.
3D mapping
LiDAR system
map version control
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