SubT-MRS Dataset: Pushing SLAM Towards All-weather Environments

TL;DR

The paper introduces SubT-MRS, a comprehensive all-weather SLAM dataset with diverse environments and sensors, aiming to enhance SLAM robustness and generalizability across challenging real-world conditions.

Contribution

It provides a new challenging dataset with diverse scenes, sensors, and evaluation metrics specifically designed to improve SLAM robustness in all-weather environments.

Findings

Revealed key challenges in all-weather SLAM scenarios

Identified limitations of current SLAM solutions in diverse conditions

Provided new robustness metrics for SLAM evaluation

Abstract

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.