【摘要】 将三维高斯泼溅（3DGS）整合到同时定位与地图构建（SLAM）中的传统方法往往受限于静态场景或已知相机位姿的场景。为快速准确地在动态场景中进行定位与建模，提出一种基于运动表示的跟踪和动态场景重建方法。跟踪过程通过广义迭代最邻近点（G-ICP）计算相机位姿，还使用基于光流提示的运动分割模型识别场景中的动态物体掩模以区分动态对象和静态背景；映射过程将RGB-D(Red Green Blue-Depth)数据和长距离二维轨迹作为先验数据，通过基于特殊欧几里得群运动基的三维高斯泼溅对运动物体进行建模。实验结果表明，添加光流提示的运动分割模型的提取精度较原始SAM 2模型提高了23.2%。在静态场景重建方面，所提方法在Replica数据集上取得了98 frame/s的重建速度，在TUM RGB-D数据集上取得了75 frame/s的重建速度，并且在两种数据集上均实现了高精度的场景重建；在动态场景下的轨迹估计方面，所提算法的绝对轨迹误差相对于ORB-SLAM3算法减少了78.30%，相对于RDS-SLAM算法减少了61.35%，相对于DS-SLAM算法减少了36.05%，验证了所提方法在复杂动态场景中具有良好的定位精度和鲁棒性。更多还原

【Abstract】 Traditional approaches integrating 3D Gaussian splatting(3DGS) into simultaneous localization and mapping(SLAM) are often limited to static scenes or scenarios with known camera poses. To enable fast and accurate localization and modeling in dynamic environments, this study proposes a tracking and reconstruction method based on motion representation for dynamic scenes. The tracking process computes camera poses via generalized-iterative closest point(G-ICP) and uses an optical flow-guided motion segmentation model to identify dynamic object masks for separating moving objects from static backgrounds. The mapping process leverages red green blue-depth(RGB-D) data and long-range 2D trajectories as data priors, modeling moving objects using the 3D Gaussian splatting framework based on special Euclidean motion bases. Experimental results demonstrate that the optical flow-guided motion segmentation model improves extraction precision by 23.2% compared to the original SAM 2 model. For static scene reconstruction, the proposed method achieves real-time modeling at 98 frame/s on the Replica dataset and 75 frame/s on the TUM RGB-D dataset, and high-precision scene reconstruction is realized on both datasets. In dynamic scenarios, the proposed algorithm reduces absolute trajectory error by 78.30% compared to ORB-SLAM3 algorithm, 61.35% compared to RDS-SLAM algorithm, and 36.05% compared to DS-SLAM algorithm, verifying its superior localization precision and robustness in complex dynamic environments.更多还原