The development and evaluation of Lidar-Inertial Odometry (LIO) and Simultaneous Localization and Mapping (SLAM) systems requires a precise ground truth. The Global Navigation Satellite System (GNSS) is often used as a foundation for this, but its signals can be unreliable in obstructed environments due to multi-path effects or loss-of-signal. While existing datasets compensate for the sporadic loss of GNSS signals by incorporating Inertial Measurement Unit (IMU) measurements, the commonly used Micro-Electro-Mechanical Systems (MEMS) or Fiber Optic Gyroscope (FOG)-based systems do not permit the prolonged study of GNSS-denied environments. To close this gap, we present Odyssey, a LIO dataset with a focus on GNSS-denied environments such as tunnels and parking garages as well as other underrepresented, yet ubiquitous situations such as stop-and-go-traffic, bumpy roads and wide open fields. Our ground truth is derived from a navigation-grade Inertial Navigation System (INS) equipped with a Ring Laser Gyroscope (RLG), offering exceptional bias stability characteristics compared to IMUs used in existing datasets and enabling the prolonged and accurate study of GNSS-denied environments. This makes Odyssey the first publicly available dataset featuring a RLG-based INS. Besides providing data for LIO, we also support other tasks, such as place recognition, through the threefold repetition of all trajectories as well as the integration of external mapping data by providing precise geodetic coordinates. All data, dataloader and other material is available online at https://odyssey.uni-goettingen.de/ .

翻译：激光雷达-惯性里程计（LIO）与同步定位与建图（SLAM）系统的开发与评估需要精确的地面真值。全球导航卫星系统（GNSS）常被用作其基础，但在遮挡环境中，由于多径效应或信号丢失，其信号可能不可靠。现有数据集通过融合惯性测量单元（IMU）数据来补偿GNSS信号的偶发性丢失，但常用的微机电系统（MEMS）或光纤陀螺（FOG）系统无法支持对GNSS缺失环境的长期研究。为填补这一空白，我们提出了奥德赛数据集，这是一个专注于GNSS缺失环境（如隧道和停车场）以及其他代表性不足但普遍存在的场景（如启停交通、颠簸道路和开阔场地）的LIO数据集。我们的地面真值源自配备环形激光陀螺（RLG）的导航级惯性导航系统（INS），与现有数据集使用的IMU相比，具有优异的偏置稳定性特性，支持对GNSS缺失环境进行长期精确研究。这使得奥德赛成为首个公开可用的基于RLG的INS数据集。除为LIO提供数据外，我们还通过三次重复所有轨迹以及提供精确大地坐标整合外部地图数据，支持其他任务（如地点识别）。所有数据、数据加载器及相关材料均在线发布于https://odyssey.uni-goettingen.de/。