The rapid developments of mobile robotics and autonomous navigation over the years are largely empowered by public datasets for testing and upgrading, such as SLAM and localization tasks. Impressive demos and benchmark results have arisen, indicating the establishment of a mature technical framework. However, from the view point of real-world deployments, there are still critical defects of robustness in challenging environments, especially in large-scale, GNSS-denied, textural-monotonous, and unstructured scenarios. To meet the pressing validation demands in such scope, we build a novel challenging robot navigation dataset in a large botanic garden of more than 48000m2. Comprehensive sensors are employed, including high-res/rate stereo Gray&RGB cameras, rotational and forward 3D LiDARs, and low-cost and industrial-grade IMUs, all of which are well calibrated and accurately hardware-synchronized. An all-terrain wheeled robot is configured to mount the sensor suite and provide odometry data. A total of 32 long and short sequences of 2.3 million images are collected, covering scenes of thick woods, riversides, narrow paths, bridges, and grasslands that rarely appeared in previous resources. Excitedly, both highly-accurate ego-motions and 3D map ground truth are provided, along with fine-annotated vision semantics. Our goal is to contribute a high-quality dataset to advance robot navigation and sensor fusion research to a higher level.

翻译：近年来，移动机器人和自主导航技术的快速发展很大程度上得益于用于测试与升级的公开数据集（如SLAM与定位任务）。令人瞩目的演示与基准测试结果层出不穷，标志着成熟技术框架的建立。然而从实际部署角度来看，在挑战性场景（尤其是大规模、无GNSS信号、纹理单调且非结构化环境）中仍存在关键的鲁棒性缺陷。为满足此类场景下的迫切验证需求，我们在面积超过48000平方米的大型植物园中构建了一个新颖的挑战性机器人导航数据集。数据集采用了包含高分辨率/高帧率立体灰度与RGB相机、旋转与前向三维激光雷达，以及低成本工业级惯性测量单元在内的综合传感器套件，所有传感器均经过精细标定与精确硬件同步。全地形轮式机器人被配置用于搭载传感器套件并提供里程计数据。共采集32组长短序列（合计230万张图像），覆盖此前资源中罕见的茂密树林、河岸、窄径、桥梁与草地场景。令人振奋的是，本数据集不仅提供了高精度自运动轨迹与三维地图真值，还附带了精细标注的视觉语义信息。我们的目标是为推动机器人导航与传感器融合研究迈向更高水平提供一份高质量数据集。