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 sporadic GNSS loss by incorporating Inertial Measurement Unit (IMU) measurements, the commonly used systems do not permit prolonged study of GNSS-denied environments due to accumulated drift. Therefore, the diversity of such datasets is limited. To close this gap, we present Odyssey, an automotive LIO dataset featuring: (1) a ground truth derived from a navigation-grade Ring Laser Gyroscope (RLG)-based RTK/INS, offering bias stability one to four orders of magnitude better than existing automotive datasets; (2) a comprehensive collection of 36 sequences across diverse environments, enabling robust and comprehensive evaluation and (3) prolonged GNSS-denied environments, including tunnels and, previously unseen in the context of automotive benchmarks, indoor parking garages. Here, our RLG-based system enables accurate evaluation in scenarios where commonly employed systems would drift excessively. Besides providing data for LIO, Odyssey also supports place recognition tasks through threefold trajectory repetition and integration of external mapping data via precise geodetic coordinates. All data, dataloader and supplementary material are available online at https://odyssey.uni-goettingen.de/ .

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