构建多机器人多时段SLAM数据集，支持昼夜与复杂地形下的高精度建图。
DiTer++: Diverse Terrain and Multi-modal Dataset for Multi-Robot SLAM in Multi-session Environments
- 设计双机器人昼夜协同扫描，覆盖结构化与非结构化场景。
- 通过先验地图去除动态物体与离群点，实现厘米级轨迹重建。
- 适合研究多机器人、跨时段、复杂地形下SLAM算法的科研人员。
在校园等大规模环境中，结构化与非结构化空间共存，光照条件与动态物体持续变化。为应对此类场景下的大范围建图挑战，我们提出DiTer++，一个面向多机器人多时段SLAM的多样化地形与多模态数据集。其中，Agent-A与Agent-B分别在白天与夜间对指定区域进行扫描，以实现高效的大规模映射。同时，采用足式机器人实现对各类地形的无差别通行。为生成各机器人的真实轨迹，首先构建测绘级先验地图；随后剔除动态物体与离群点，并通过扫描到地图匹配提取轨迹。数据集及补充材料可访问 https://sites.google.com/view/diter-plusplus/ 获取。
原文摘要 · Abstract (English)
We encounter large-scale environments where both structured and unstructured spaces coexist, such as on campuses. In this environment, lighting conditions and dynamic objects change constantly. To tackle the challenges of large-scale mapping under such conditions, we introduce DiTer++, a diverse terrain and multi-modal dataset designed for multi-robot SLAM in multi-session environments. According to our datasets' scenarios, Agent-A and Agent-B scan the area designated for efficient large-scale mapping day and night, respectively. Also, we utilize legged robots for terrain-agnostic traversing. To generate the ground-truth of each robot, we first build the survey-grade prior map. Then, we remove the dynamic objects and outliers from the prior map and extract the trajectory through scan-to-map matching. Our dataset and supplement materials are available at https://sites.google.com/view/diter-plusplus/.
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