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3DGS-ReLoc: 3D Gaussian Splatting for Map Representation and Visual ReLocalization


แนวคิดหลัก
Utilizing 3D Gaussian Splatting for accurate map representation and visual relocalization in autonomous systems.
บทคัดย่อ

This paper introduces the 3DGS-ReLoc system, which leverages LiDAR and camera data to create detailed and geometrically accurate environmental representations. By using 3D Gaussian Splatting, the system can generate large-scale maps with high fidelity. The method initiates training with LiDAR data to improve precision in environmental modeling. To address GPU memory challenges, a strategy of dividing maps into 2D voxels and utilizing a KD tree is employed. The system demonstrates effectiveness through evaluation on the KITTI360 dataset. Various sensor fusion techniques are discussed, highlighting the importance of integrating LiDAR and camera data for autonomous navigation. The paper explores visual relocalization methods using feature-based matching and Perspective-n-Point technique to refine camera poses accurately.

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Our approach achieved a success rate of 98.2% in Seq 0 and 98.7% in Seq 1. Initial localization errors were reduced from 3.513 meters to 0.185 meters in X-axis during refinement. Relative Pose Error (RPE) showed consistent metrics with an RMSE of 0.083 across both sequences.
คำพูด
"Utilizing LiDAR data, our method initiates the training of the 3D Gaussian Splatting representation." "Our primary aim is establishing a dependable mapping system for visual relocalization." "The NCC metric maintains its effectiveness even with an error margin of up to 10 meters."

ข้อมูลเชิงลึกที่สำคัญจาก

by Peng Jiang,G... ที่ arxiv.org 03-19-2024

https://arxiv.org/pdf/2403.11367.pdf
3DGS-ReLoc

สอบถามเพิ่มเติม

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