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（Neural Radiance Fields, NeRFs）在同时定位与地图构建（Simultaneous Localization and Mapping, SLAM）任务中展现出地图表示的通用性与鲁棒性。本文对NICE-SLAM——一种近期最先进的、能生成高质量NeRF地图的基于NeRF的SLAM算法——进行了扩展。然而，受硬件条件限制，生成这些地图所需的迭代次数常导致NICE-SLAM运行速度低于实时要求。此外，其估计轨迹的精度难以与经典SLAM方法竞争。最后，NICE-SLAM需在运行前定义覆盖待测环境的网格，这使得其难以扩展至未预见的场景。本文旨在通过提升鲁棒性与跟踪精度、泛化地图表示以处理无约束环境，从而增强NICE-SLAM的开放世界适应能力。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。研究表明，根据可用资源的不同，这些改进能将跟踪精度提升85%至97%，同时在视觉信息超出预设网格边界的环境中也改善了建图效果。