  Simultaneous localization and mapping (SLAM) is a fundamental task for
numerous applications such as autonomous navigation and exploration. Despite
many SLAM datasets have been released, current SLAM solutions still struggle to
have sustained and resilient performance. One major issue is the absence of
high-quality datasets including diverse all-weather conditions and a reliable
metric for assessing robustness. This limitation significantly restricts the
scalability and generalizability of SLAM technologies, impacting their
development, validation, and deployment. To address this problem, we present
SubT-MRS, an extremely challenging real-world dataset designed to push SLAM
towards all-weather environments to pursue the most robust SLAM performance. It
contains multi-degraded environments including over 30 diverse scenes such as
structureless corridors, varying lighting conditions, and perceptual obscurants
like smoke and dust; multimodal sensors such as LiDAR, fisheye camera, IMU, and
thermal camera; and multiple locomotions like aerial, legged, and wheeled
robots. We develop accuracy and robustness evaluation tracks for SLAM and
introduced novel robustness metrics. Comprehensive studies are performed,
revealing new observations, challenges, and opportunities for future research.
