  We present SLAIM - Simultaneous Localization and Implicit Mapping. We propose
a novel coarse-to-fine tracking model tailored for Neural Radiance Field SLAM
(NeRF-SLAM) to achieve state-of-the-art tracking performance. Notably, existing
NeRF-SLAM systems consistently exhibit inferior tracking performance compared
to traditional SLAM algorithms. NeRF-SLAM methods solve camera tracking via
image alignment and photometric bundle-adjustment. Such optimization processes
are difficult to optimize due to the narrow basin of attraction of the
optimization loss in image space (local minima) and the lack of initial
correspondences. We mitigate these limitations by implementing a Gaussian
pyramid filter on top of NeRF, facilitating a coarse-to-fine tracking
optimization strategy. Furthermore, NeRF systems encounter challenges in
converging to the right geometry with limited input views. While prior
approaches use a Signed-Distance Function (SDF)-based NeRF and directly
supervise SDF values by approximating ground truth SDF through depth
measurements, this often results in suboptimal geometry. In contrast, our
method employs a volume density representation and introduces a novel KL
regularizer on the ray termination distribution, constraining scene geometry to
consist of empty space and opaque surfaces. Our solution implements both local
and global bundle-adjustment to produce a robust (coarse-to-fine) and accurate
(KL regularizer) SLAM solution. We conduct experiments on multiple datasets
(ScanNet, TUM, Replica) showing state-of-the-art results in tracking and in
reconstruction accuracy.
