用双二次曲面与高斯点云实现高效精准的多物体建图
DQO-MAP: Dual Quadrics Multi-Object mapping with Gaussian Splatting
- 融合二次曲面与高斯点云，分别用于精确定位和高质量重建
- 在多个数据集上达到高精度重建与实时姿态估计性能
- 适合机器人导航等需快速识别物体的应用场景
准确的物体感知对机器人应用（如物体导航）至关重要。本文提出 DQO-MAP，一种新型的物体-SLAM 系统，可无缝集成物体位姿估计与重建。采用 3D Gaussian Splatting 实现高保真物体重建，利用二次曲面进行精确物体位姿估计。两者管理在 CPU 上完成，优化在 GPU 上执行，显著提升系统效率。通过为每个物体分配唯一 ID，系统可快速从场景中提取目标物体。在物体重建与位姿估计上的大量实验表明，DQO-MAP 在精度、重建质量与计算效率方面均表现优异。代码与数据集已公开：https://github.com/LiHaoy-ux/DQO-MAP。
原文摘要 · Abstract (English)
Accurate object perception is essential for robotic applications such as object navigation. In this paper, we propose DQO-MAP, a novel object-SLAM system that seamlessly integrates object pose estimation and reconstruction. We employ 3D Gaussian Splatting for high-fidelity object reconstruction and leverage quadrics for precise object pose estimation. Both of them management is handled on the CPU, while optimization is performed on the GPU, significantly improving system efficiency. By associating objects with unique IDs, our system enables rapid object extraction from the scene. Extensive experimental results on object reconstruction and pose estimation demonstrate that DQO-MAP achieves outstanding performance in terms of precision, reconstruction quality, and computational efficiency. The code and dataset are available at: https://github.com/LiHaoy-ux/DQO-MAP.
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