Previous attempts to integrate Neural Radiance Fields (NeRF) into Simultaneous Localization and Mapping (SLAM) framework either rely on the assumption of static scenes or treat dynamic objects as outliers. However, most of real-world scenarios is dynamic. In this paper, we propose a time-varying representation to track and reconstruct the dynamic scenes. Our system simultaneously maintains two processes, tracking process and mapping process. For tracking process, the entire input images are uniformly sampled and training of the RGB images are self-supervised. For mapping process, we leverage know masks to differentiate dynamic objects and static backgrounds, and we apply distinct sampling strategies for two types of areas. The parameters optimization for both processes are made up by two stages, the first stage associates time with 3D positions to convert the deformation field to the canonical field. And the second associates time with 3D positions in canonical field to obtain colors and Signed Distance Function (SDF). Besides, We propose a novel keyframe selection strategy based on the overlapping rate. We evaluate our approach on two publicly available synthetic datasets and validate that our method is more effective compared to current state-of-the-art dynamic mapping methods.

翻译：先前将神经辐射场（NeRF）集成到同步定位与建图（SLAM）框架中的尝试，要么依赖于静态场景假设，要么将动态物体视为离群点。然而，大多数真实场景是动态的。本文提出一种时间变化表示法来跟踪与重建动态场景。我们的系统同时维护两个进程：跟踪进程与建图进程。针对跟踪进程，我们对全部输入图像进行均匀采样，并对RGB图像进行自监督训练；针对建图进程，我们利用已知掩码区分动态物体与静态背景，并对两类区域采用差异化的采样策略。两个进程的参数优化均包含两个阶段：第一阶段将时间与三维位置关联，将形变场映射至规范场；第二阶段将规范场中的时间与三维位置关联，以获取颜色与符号距离函数（SDF）。此外，我们提出基于重叠率的创新关键帧选择策略。我们在两个公开合成数据集上评估了该方法，验证了相较于当前最先进的动态建图方法，本方法具有更优效果。