MGSO: Monocular Real-time Photometric SLAM with Efficient 3D Gaussian Splatting

• URL: http://arxiv.org/abs/2409.13055v1
• Date: Thu, 19 Sep 2024 19:07:05 GMT
• Title: MGSO: Monocular Real-time Photometric SLAM with Efficient 3D Gaussian Splatting
• Authors: Yan Song Hu, Nicolas Abboud, Muhammad Qasim Ali, Adam Srebrnjak Yang, Imad Elhajj, Daniel Asmar, Yuhao Chen, John S. Zelek,
• Abstract summary: We present Monocular GSO, a novel real-time SLAM system that integrates photometric SLAM with 3DGS. Our system generates reconstructions with a balance of quality, memory efficiency, and speed that outperforms the state-of-the-art. Not only do we surpass contemporary systems, but experiments also show that we maintain our performance on laptop hardware.
• Score: 8.577428137443246
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Real-time SLAM with dense 3D mapping is computationally challenging, especially on resource-limited devices. The recent development of 3D Gaussian Splatting (3DGS) offers a promising approach for real-time dense 3D reconstruction. However, existing 3DGS-based SLAM systems struggle to balance hardware simplicity, speed, and map quality. Most systems excel in one or two of the aforementioned aspects but rarely achieve all. A key issue is the difficulty of initializing 3D Gaussians while concurrently conducting SLAM. To address these challenges, we present Monocular GSO (MGSO), a novel real-time SLAM system that integrates photometric SLAM with 3DGS. Photometric SLAM provides dense structured point clouds for 3DGS initialization, accelerating optimization and producing more efficient maps with fewer Gaussians. As a result, experiments show that our system generates reconstructions with a balance of quality, memory efficiency, and speed that outperforms the state-of-the-art. Furthermore, our system achieves all results using RGB inputs. We evaluate the Replica, TUM-RGBD, and EuRoC datasets against current live dense reconstruction systems. Not only do we surpass contemporary systems, but experiments also show that we maintain our performance on laptop hardware, making it a practical solution for robotics, A/R, and other real-time applications.

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