3 accepted papers
In range-based SLAM systems, localization accuracy depends on the quality of geometric maps. Sparse LiDAR scans and noisy depth from RGB-D sensors often yield incomplete or inaccurate reconstructions that degrade pose estimation. Appearance and semantic cues, readily available from onboard RGB and p
We propose 3D-SLNR, a new and ultra-lightweight neural representation with outstanding performance for large-scale 3D mapping. The representation defines a global signed distance function (SDF) in near-surface space based on a set of band-limited local SDFs anchored at support points sampled from po…
Large-scale 3D mapping nowadays is a research hotspot in robotics. A greatly concerning issue is reconstructing high-accuracy maps in a hardware environment with limited memory. To address this problem, we propose a novel implicit neural mapping approach with higher accuracy and less memory. It firs