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）在同时定位与地图构建（SLAM）任务中为地图表示提供了通用性和鲁棒性。本文对NICE-SLAM进行了扩展，该算法是近期基于NeRF的SLAM领域最先进的算法之一，能够生成高质量的NeRF地图。然而，受限于硬件条件，生成这些地图所需的迭代次数往往导致NICE-SLAM的运行速度低于实时要求。此外，其估计的轨迹也难以与经典SLAM方法相媲美。最后，NICE-SLAM需要在运行时之前预定义覆盖环境的网格，这使其难以扩展到未观察过的场景。本文旨在通过提升鲁棒性和跟踪精度，并泛化地图表示以处理无约束环境，增强NICE-SLAM在开放世界中的适用性。具体改进包括：优化测量不确定性处理、融合运动信息，以及将地图建模为显式的前景与背景。实验表明，根据可用资源的不同，这些改进可使跟踪精度提升85%至97%，同时在地图构建中也能更好地处理超出预定义网格范围的视觉信息。