GeomGS: LiDAR-Guided Geometry-Aware Gaussian Splatting for Robot Localization

• URL: http://arxiv.org/abs/2501.13417v1
• Date: Thu, 23 Jan 2025 06:43:38 GMT
• Title: GeomGS: LiDAR-Guided Geometry-Aware Gaussian Splatting for Robot Localization
• Authors: Jaewon Lee, Mangyu Kong, Minseong Park, Euntai Kim,
• Abstract summary: We propose a novel 3DGS method called Geometry-Aware Gaussian Splatting (GeomGS) Our GeomGS demonstrates state-of-the-art geometric and localization performance across several benchmarks, while also improving photometric performance.
• Score: 20.26969580492428
• License:
• Abstract: Mapping and localization are crucial problems in robotics and autonomous driving. Recent advances in 3D Gaussian Splatting (3DGS) have enabled precise 3D mapping and scene understanding by rendering photo-realistic images. However, existing 3DGS methods often struggle to accurately reconstruct a 3D map that reflects the actual scale and geometry of the real world, which degrades localization performance. To address these limitations, we propose a novel 3DGS method called Geometry-Aware Gaussian Splatting (GeomGS). This method fully integrates LiDAR data into 3D Gaussian primitives via a probabilistic approach, as opposed to approaches that only use LiDAR as initial points or introduce simple constraints for Gaussian points. To this end, we introduce a Geometric Confidence Score (GCS), which identifies the structural reliability of each Gaussian point. The GCS is optimized simultaneously with Gaussians under probabilistic distance constraints to construct a precise structure. Furthermore, we propose a novel localization method that fully utilizes both the geometric and photometric properties of GeomGS. Our GeomGS demonstrates state-of-the-art geometric and localization performance across several benchmarks, while also improving photometric performance.

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