Neural Radiance Fields (NeRFs) offer versatility and robustness in map representations for Simultaneous Localization and Mapping (SLAM) tasks. This paper extends NICE-SLAM, a recent state-of-the-art NeRF-based SLAM algorithm capable of producing high quality NeRF maps. However, depending on the hardware used, the required number of iterations to produce these maps often makes NICE-SLAM run at less than real time. Additionally, the estimated trajectories fail to be competitive with classical SLAM approaches. Finally, NICE-SLAM requires a grid covering the considered environment to be defined prior to runtime, making it difficult to extend into previously unseen scenes. This paper seeks to make NICE-SLAM more open-world-capable by improving the robustness and tracking accuracy, and generalizing the map representation to handle unconstrained environments. This is done by improving measurement uncertainty handling, incorporating motion information, and modelling the map as having an explicit foreground and background. It is shown that these changes are able to improve tracking accuracy by 85% to 97% depending on the available resources, while also improving mapping in environments with visual information extending outside of the predefined grid.

翻译：神经辐射场（NeRFs）在同步定位与地图构建（SLAM）任务中提供了地图表示的通用性和鲁棒性。本文对NICE-SLAM进行了扩展，NICE-SLAM是一种最新基于NeRF的先进SLAM算法，能够生成高质量NeRF地图。然而，根据所使用的硬件，生成这些地图所需的迭代次数往往使NICE-SLAM的运行速度低于实时要求。此外，其估计轨迹无法与经典SLAM方法相竞争。最后，NICE-SLAM需要在运行前预先定义覆盖所考虑环境的网格，这使其难以扩展到先前未见的场景。本文旨在通过改进鲁棒性和跟踪精度，并泛化地图表示以处理无约束环境，使NICE-SLAM具备更强的开放世界能力。具体方法包括改进测量不确定性处理、融入运动信息，以及将地图建模为明确的前景和背景。研究表明，这些改进能够根据可用资源将跟踪精度提升85%至97%，同时改善在视觉信息超出预定义网格范围的环境中的地图构建效果。