The rapid developments of mobile robotics and autonomous navigation over the years are largely empowered by public datasets for testing and upgrading, such as sensor odometry and SLAM tasks. Impressive demos and benchmark scores have arisen, which may suggest the maturity of existing navigation techniques. However, these results are primarily based on moderate structured scenario testing. When transitioning to challenging unstructured environments, especially in GNSS-denied, texture-monotonous, and dense-vegetated natural fields, their performance can hardly sustain at a high level and requires further validation and improvement. To bridge this gap, we build a novel robot navigation dataset in a luxuriant botanic garden of more than 48000m2. Comprehensive sensors are used, including Gray and RGB stereo cameras, spinning and MEMS 3D LiDARs, and low-cost and industrial-grade IMUs, all of which are well calibrated and hardware-synchronized. An all-terrain wheeled robot is employed for data collection, traversing through thick woods, riversides, narrow trails, bridges, and grasslands, which are scarce in previous resources. This yields 33 short and long sequences, forming 17.1km trajectories in total. Excitedly, both highly-accurate ego-motions and 3D map ground truth are provided, along with fine-annotated vision semantics. We firmly believe that our dataset can advance robot navigation and sensor fusion research to a higher level.

翻译：近年来，移动机器人与自主导航技术的快速发展，在很大程度上得益于用于测试和升级的公开数据集（如传感器里程计与SLAM任务）。令人印象深刻的演示和基准分数表明现有导航技术已趋成熟。然而，这些结果主要基于中等结构化场景测试。当面临具有挑战性的非结构化环境，尤其是全球导航卫星系统拒止、纹理单调且植被茂密的自然场景时，其性能难以维持高水平，仍需进一步验证与提升。为弥补这一不足，我们在一个占地超48000平方米的繁茂植物园中构建了新型机器人导航数据集。数据集使用了完备的传感器，包括灰度与RGB立体相机、旋转式与MEMS 3D激光雷达、低成本与工业级惯性测量单元，所有设备均经良好标定并实现硬件同步。采用全地形轮式机器人进行数据采集，穿越此前资源匮乏的茂密林地、河岸、窄径、桥梁及草地，共生成33组长短序列，形成总长17.1公里的运动轨迹。值得振奋的是，我们同时提供了高精度自运动轨迹与三维地图真值，并附有精细标注的视觉语义信息。我们坚信，本数据集将推动机器人导航与传感器融合研究迈向更高水平。