This paper presents a collaborative implicit neural simultaneous localization and mapping (SLAM) system with RGB-D image sequences, which consists of complete front-end and back-end modules including odometry, loop detection, sub-map fusion, and global refinement. In order to enable all these modules in a unified framework, we propose a novel neural point based 3D scene representation in which each point maintains a learnable neural feature for scene encoding and is associated with a certain keyframe. Moreover, a distributed-to-centralized learning strategy is proposed for the collaborative implicit SLAM to improve consistency and cooperation. A novel global optimization framework is also proposed to improve the system accuracy like traditional bundle adjustment. Experiments on various datasets demonstrate the superiority of the proposed method in both camera tracking and mapping.

翻译：本文提出一种基于RGB-D图像序列的协作式隐式神经同步定位与地图构建（SLAM）系统，其包含完整的里程计、闭环检测、子图融合与全局优化等前端与后端模块。为实现所有模块的统一框架，我们提出了一种新颖的基于神经点的三维场景表示方法，其中每个点维护一个可学习的神经特征用于场景编码，并与特定关键帧关联。此外，针对协作式隐式SLAM提出了一种分布式到集中式的学习策略，以提升系统一致性与协作能力。同时引入一种创新的全局优化框架，通过类似传统光束法平差的方式提高系统精度。在多个数据集上的实验结果表明，所提方法在相机跟踪与地图构建方面均具有优越性。