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. Our extensive experiments on two standard and recent datasets, Replica and ScanNet, show that ESLAM improves the accuracy of 3D reconstruction and camera localization of state-of-the-art dense visual SLAM methods by more than 50%, while it runs up to $\times$10 faster and does not require any pre-training.

ESLAM 是一种用于同时定位和建图的高效隐式神经表示方法，它基于多尺度轴对齐法线特征面和浅解码器，使用神经辐射场技术，将连续空间中的特征解码成距离场和 RGB 值，并成功地在三个标准数据集上实现了超过 50% 的 3D 重建和相机定位精度提升，速度提升了10倍。

ESLAM: 基于符号距离场混合表示的高效密集SLAM系统