Mobile robots navigating in outdoor environments frequently encounter the issue of undesired traces left by dynamic objects and manifested as obstacles on map, impeding robots from achieving accurate localization and effective navigation. To tackle the problem, a novel map construction framework based on 3D region-wise hash map structure (RH-Map) is proposed, consisting of front-end scan fresher and back-end removal modules, which realizes real-time map construction and online dynamic object removal (DOR). First, a two-layer 3D region-wise hash map structure of map management is proposed for effective online DOR. Then, in scan fresher, region-wise ground plane estimation (R-GPE) is adopted for estimating and preserving ground information and Scan-to-Map Removal (S2M-R) is proposed to discriminate and remove dynamic regions. Moreover, the lightweight back-end removal module maintaining keyframes is proposed for further DOR. As experimentally verified on SemanticKITTI, our proposed framework yields promising performance on online DOR of map construction compared with the state-of-the-art methods. And we also validate the proposed framework in real-world environments.

翻译：在户外环境中导航的移动机器人常遇到动态物体遗留的轨迹痕迹在地图上表现为障碍物的问题，这阻碍了机器人实现精确定位与有效导航。为解决该问题，本文提出一种基于三维区域哈希地图结构（RH-Map）的新型地图构建框架，包含前端扫描更新模块与后端移除模块，实现实时地图构建与在线动态物体移除（DOR）。首先，提出一种双层三维区域哈希地图结构管理机制，用于有效在线DOR。随后，在扫描更新模块中，采用区域地面平面估计（R-GPE）来估计并保留地面信息，并提出扫描-地图移除（S2M-R）方法以区分并移除动态区域。此外，提出轻量级后端移除模块维护关键帧，用于进一步DOR。基于SemanticKITTI数据集的实验验证表明，与现有最优方法相比，本框架在地图构建的在线DOR任务中展现出优异性能。同时，我们在真实环境中也验证了所提框架的有效性。