This paper presents an efficient online framework to solve the well-known
semantic Visual Simultaneous Localization and Mapping (V-SLAM) problem for
indoor scenes leveraging the advantages of neural implicit scene
representation. Existing methods on similar lines, such as NICE-SLAM, has some
critical practical limitations to put to use for such an important indoor scene
understanding problem. To this end, we contend for the following proposition
for modern semantic V-SLAM contrary to existing methods assuming RGB-D frames
as input (i) For a rigid scene, robust and accurate camera motion could be
computed with disentangled tracking and 3D mapping pipeline. (ii) Using neural
fields, a dense and multifaceted scene representation of SDF, semantics, RGB,
and depth is provided memory efficiently. (iii) Rather than using every frame,
we demonstrate that the set of keyframes is sufficient to learn excellent scene
representation, thereby improving the pipeline's train time. (iv) Multiple
local mapping networks could be used to extend the pipeline for large-scale
scenes. We show via extensive experiments on several popular benchmark datasets
that our approach offers accurate tracking, mapping, and semantic labeling at
test time even with noisy and highly sparse depth measurements. Later in the
paper, we show that our pipeline can easily extend to RGB image input. Overall,
the proposed pipeline offers a favorable solution to an important scene
understanding task that can assist in diverse robot visual perception and
related problems.

提出了一种利用神经隐式场表示解决室内场景语义 V-SLAM 问题的在线框架，并通过多项实验表明其在测试时间具有精确的跟踪、地图绘制和语义标注能力，能够扩展到 RGB 图像输入，为机器人视觉感知及相关问题提供了可行的解决方案。