arXiv 2409.201112024-09-30cs.RO

利用回环检测的鲁棒高斯泼溅SLAM

Robust Gaussian Splatting SLAM by Leveraging Loop Closure

School of Communication Engineering, Hangzhou Dianzi University（杭州电子科技大学通信工程学院）；Lishui Institute of Hangzhou Dianzi University（杭州电子科技大学丽水研究院）；School of Automation, Hangzhou Dianzi University（杭州电子科技大学自动化学院）；Faculty of Applied Sciences, Macao Polytechnic University（澳门理工大学应用科学学院）；School of Electrical and Electronics Engineering, Nanyang Technological University（南洋理工大学电气与电子工程学院）；School of Software, Tsinghua University（清华大学软件学院）；Technical University of Munich（慕尼黑工业大学）

Zunjie Zhu, Youxu Fang, Xin Li, Chengang Yan, Feng Xu, Chau Yuen, Yanyan Li

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AI总结：

本文针对旋转式多RGB-D相机下的高斯泼溅SLAM跟踪漂移问题，提出结合锚定帧分类、共视渲染回环检测、位姿图优化与光束法平差的鲁棒架构，在定位与渲染上优于现有方法。

摘要：

3D高斯泼溅算法在新视角渲染应用中表现出色，并已被用于扩展传统SLAM系统的能力。然而，当前的高斯泼溅SLAM方法主要针对手持式RGB或RGB-D传感器设计，当用于旋转式RGB-D相机配置时，会出现跟踪漂移问题。本文提出了一种鲁棒的高斯泼溅SLAM架构，利用旋转式多RGB-D相机的输入来实现精确定位和逼真渲染性能。精心设计的高斯泼溅回环检测模块有效解决了传统高斯泼溅SLAM系统中累积的跟踪与建图误差问题。首先，每个高斯与一个锚定帧关联，并根据其时间戳被分类为历史高斯或新高斯。通过在同一视点渲染不同类型的高斯，所提出的回环检测策略同时考虑了共视关系和不同的渲染结果。此外，提出了一种回环优化方法来消除相机位姿漂移并保持3D高斯模型的高质量。该方法使用轻量级位姿图优化算法来校正位姿漂移，并根据优化后的位姿更新高斯。另外，光束法平差方案利用光度约束和几何约束进一步优化相机位姿，最终增强场景的全局一致性。在合成数据集和真实世界数据集上的定量与定性评估表明，我们的方法在相机位姿估计和新视角渲染任务上优于最先进的方法。代码将向社区开源。

英文摘要：

3D Gaussian Splatting algorithms excel in novel view rendering applications and have been adapted to extend the capabilities of traditional SLAM systems. However, current Gaussian Splatting SLAM methods, designed mainly for hand-held RGB or RGB-D sensors, struggle with tracking drifts when used with rotating RGB-D camera setups. In this paper, we propose a robust Gaussian Splatting SLAM architecture that utilizes inputs from rotating multiple RGB-D cameras to achieve accurate localization and photorealistic rendering performance. The carefully designed Gaussian Splatting Loop Closure module effectively addresses the issue of accumulated tracking and mapping errors found in conventional Gaussian Splatting SLAM systems. First, each Gaussian is associated with an anchor frame and categorized as historical or novel based on its timestamp. By rendering different types of Gaussians at the same viewpoint, the proposed loop detection strategy considers both co-visibility relationships and distinct rendering outcomes. Furthermore, a loop closure optimization approach is proposed to remove camera pose drift and maintain the high quality of 3D Gaussian models. The approach uses a lightweight pose graph optimization algorithm to correct pose drift and updates Gaussians based on the optimized poses. Additionally, a bundle adjustment scheme further refines camera poses using photometric and geometric constraints, ultimately enhancing the global consistency of scenarios. Quantitative and qualitative evaluations on both synthetic and real-world datasets demonstrate that our method outperforms state-of-the-art methods in camera pose estimation and novel view rendering tasks. The code will be open-sourced for the community.

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