GEMOR-GS: Global Extrinsic-calibration-refined Mapping and Online Relocalization for 3D Gaussian-Splatting SLAM
Overview
This research introduces GEMOR-GS, a system designed to create photorealistic and metrically consistent world representations using 3D Gaussian splatting for LiDAR-inertial-visual SLAM. The method refines the extrinsic calibration between LiDAR and cameras offline, then alternately refines keyframe poses and Gaussian parameters using differentiable rendering and projection objectives. Experiments on self-collected and public datasets show that GEMOR-GS improves map geometric accuracy by 14.3-19.9 dB over online maps, increases PSNR by approximately 9 dB compared to Gaussian-LIC2, and achieves 86-100% relocalization success, serving as a dependable world model for robot perception and decision-making.
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Reported quantity · PSNR improvement dB: 9 other · Basis not reported
“alization, and Gaussian RGB-D warp refinement. Experiments on one self-collected and five public sequences quantify each element: the offline reconstruction wins all paired keyframes against the online map by 14.3--19.9 dB, improves PSNR by approximately 9 dB over Gaussian-LIC2, and achieves 86--100% held-out relocalization success tied to extrinsic quality. The results show that GEMOR-GS improves the geometric accur”
Exact source · revision 1Source owner not reported
Reported quantity · offline reconstruction PSNR gain dB: 14.3 other · Basis not reported · Differing source assertions
“alization, and Gaussian RGB-D warp refinement. Experiments on one self-collected and five public sequences quantify each element: the offline reconstruction wins all paired keyframes against the online map by 14.3--19.9 dB, improves PSNR by approximately 9 dB over Gaussian-LIC2, and achieves 86--100% held-out relocalization success tied to extrinsic quality. The results show that GEMOR-GS improves the geometric accur”
Exact source · revision 1Source owner not reported
Reported quantity · offline reconstruction PSNR gain dB: 19.9 other · Basis not reported · Differing source assertions
“alization, and Gaussian RGB-D warp refinement. Experiments on one self-collected and five public sequences quantify each element: the offline reconstruction wins all paired keyframes against the online map by 14.3--19.9 dB, improves PSNR by approximately 9 dB over Gaussian-LIC2, and achieves 86--100% held-out relocalization success tied to extrinsic quality. The results show that GEMOR-GS improves the geometric accur”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalGEMOR-GS: Global Extrinsic-calibration-refined Mapping and Online Relocalization for 3D Gaussian-Splatting SLAM
This research introduces GEMOR-GS, a system designed to create photorealistic and metrically consistent world representations using 3D Gaussian splatting for LiDAR-inertial-visual SLAM. The method refines the extrinsic calibration between LiDAR and cameras offline, then alternately refines keyframe poses and Gaussian parameters using differentiable rendering and projection objectives. Experiments on self-collected and public datasets show that GEMOR-GS improves map geometric accuracy by 14.3-19.9 dB over online maps, increases PSNR by approximately 9 dB compared to Gaussian-LIC2, and achieves 86-100% relocalization success, serving as a dependable world model for robot perception and decision-making.
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