  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.
