  Visual Simultaneous Localization and Mapping (vSLAM) is a widely used
technique in robotics and computer vision that enables a robot to create a map
of an unfamiliar environment using a camera sensor while simultaneously
tracking its position over time. In this paper, we propose a novel RGBD vSLAM
algorithm that can learn a memory-efficient, dense 3D geometry, and semantic
segmentation of an indoor scene in an online manner. Our pipeline combines
classical 3D vision-based tracking and loop closing with neural fields-based
mapping. The mapping network learns the SDF of the scene as well as RGB, depth,
and semantic maps of any novel view using only a set of keyframes.
Additionally, we extend our pipeline to large scenes by using multiple local
mapping networks. Extensive experiments on well-known benchmark datasets
confirm that our approach provides robust tracking, mapping, and semantic
labeling even with noisy, sparse, or no input depth. Overall, our proposed
algorithm can greatly enhance scene perception and assist with a range of robot
control problems.
