Real-time and high-quailty dense mapping is essential for robots to perform fine tasks. However, most existing methods can not achieve both speed and quality. Recent works have shown that implicit neural representations of 3D scenes can produce remarkable results, but they are limited to small scenes and lack real-time performance. To address these limitations, we propose a real-time scalable mapping method using robot-centric implicit representation. We train implicit features with a multi-resolution local map and decode them as signed distance values through a shallow neural network. We maintain the learned features in a scalable manner using a global map that consists of a hash table and a submap set. We exploit the characteristics of the local map to achieve highly efficient training and mitigate the catastrophic forgetting problem in incremental implicit mapping. Extensive experiments validate that our method outperforms existing methods in reconstruction quality, real-time performance, and applicability. The code of our system will be available at \url{https://github.com/HITSZ-NRSL/RIM.git}.

翻译：实时且高质量的稠密建图对于机器人执行精细任务至关重要。然而，现有大多数方法无法兼顾速度与质量。近期研究表明，三维场景的隐式神经表征能产生显著效果，但此类方法局限于小范围场景且缺乏实时性能。为解决这些限制，我们提出了一种基于机器人中心隐式表征的实时可扩展建图方法。我们利用多分辨率局部地图训练隐式特征，并通过浅层神经网络将其解码为有符号距离值。通过由哈希表与子地图集组成的全局地图，我们以可扩展方式维护所学习的特征。我们利用局部地图的特性实现高效训练，并缓解增量式隐式建图中的灾难性遗忘问题。大量实验验证了本方法在重建质量、实时性能及适用性方面均优于现有方法。本系统代码将发布于 \url{https://github.com/HITSZ-NRSL/RIM.git}。