Implicit reconstruction of ESDF (Euclidean Signed Distance Field) involves training a neural network to regress the signed distance from any point to the nearest obstacle, which has the advantages of lightweight storage and continuous querying. However, existing algorithms usually rely on conflicting raw observations as training data, resulting in poor map performance. In this paper, we propose LGSDF, an ESDF continual Global learning algorithm aided by Local updating. At the front end, axis-aligned grids are dynamically updated by pre-processed sensor observations, where incremental fusion alleviates estimation error caused by limited viewing directions. At the back end, a randomly initialized implicit ESDF neural network performs continual self-supervised learning guided by these grids to generate smooth and continuous maps. The results on multiple scenes show that LGSDF can construct more accurate ESDF maps and meshes compared with SOTA (State Of The Art) explicit and implicit mapping algorithms. The source code of LGSDF is publicly available at https://github.com/BIT-DYN/LGSDF.

翻译：隐式重建ESDF（欧几里得符号距离场）涉及训练神经网络以回归任意点到最近障碍物的符号距离，具有轻量化存储和连续查询的优势。然而，现有算法通常依赖相互冲突的原始观测作为训练数据，导致地图性能不佳。本文提出LGSDF，一种借助局部更新的ESDF持续全局学习算法。在前端，通过预处理后的传感器观测动态更新轴对齐网格，其中增量融合缓解了有限视角方向导致的估计误差；在后端，随机初始化的隐式ESDF神经网络在这些网格引导下进行持续自监督学习，生成光滑且连续的地图。在多个场景上的结果表明，与当前最先进的显式和隐式映射算法相比，LGSDF能够构建更精确的ESDF地图和网格。LGSDF的源代码已在https://github.com/BIT-DYN/LGSDF 公开。