DQO-MAP: Dual Quadrics Multi-Object mapping with Gaussian Splatting
This work addresses object navigation for robotics, but it appears incremental as it combines existing techniques like Gaussian Splatting and quadrics in a new system.
The paper tackled the problem of accurate object perception for robotics by proposing DQO-MAP, an object-SLAM system that integrates object pose estimation and reconstruction, achieving outstanding performance in precision, reconstruction quality, and computational efficiency.
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.