ESLAM: Efficient Dense SLAM System Based on Hybrid Representation of Signed Distance Fields

Understand

We present ESLAM, an efficient implicit neural representation method for Simultaneous Localization and Mapping (SLAM).

• ESLAM reads RGB-D frames with unknown camera poses in a sequential manner and incrementally reconstructs the scene representation while estimating the current camera position in the scene.
• We incorporate the latest advances in Neural Radiance Fields (NeRF) into a SLAM system, resulting in an efficient and accurate dense visual SLAM method.
• Our scene representation consists of multi-scale axis-aligned perpendicular feature planes and shallow decoders that, for each point in the continuous space, decode the interpolated features into Truncated Signed Distance Field (TSDF) and RGB values.