  In this paper, we introduce FMapping, an efficient neural field mapping
framework that facilitates the continuous estimation of a colorized point cloud
map in real-time dense RGB SLAM. To achieve this challenging goal without
depth, a hurdle is how to improve efficiency and reduce the mapping uncertainty
of the RGB SLAM system. To this end, we first build up a theoretical analysis
by decomposing the SLAM system into tracking and mapping parts, and the mapping
uncertainty is explicitly defined within the frame of neural representations.
Based on the analysis, we then propose an effective factorization scheme for
scene representation and introduce a sliding window strategy to reduce the
uncertainty for scene reconstruction. Specifically, we leverage the factorized
neural field to decompose uncertainty into a lower-dimensional space, which
enhances robustness to noise and improves training efficiency. We then propose
the sliding window sampler to reduce uncertainty by incorporating coherent
geometric cues from observed frames during map initialization to enhance
convergence. Our factorized neural mapping approach enjoys some advantages,
such as low memory consumption, more efficient computation, and fast
convergence during map initialization. Experiments on two benchmark datasets
show that our method can update the map of high-fidelity colorized point clouds
around 2 seconds in real time while requiring no customized CUDA kernels.
Additionally, it utilizes x20 fewer parameters than the most concise neural
implicit mapping of prior methods for SLAM, e.g., iMAP [ 31] and around x1000
fewer parameters than the state-of-the-art approach, e.g., NICE-SLAM [ 42]. For
more details, please refer to our project homepage:
https://vlis2022.github.io/fmap/.
