MS-DOTT: A Multi-Modal Dataset for Off-Road Traversability Estimation

Abstract

Autonomous robots are gaining traction in off-road environments, driven by their rising demand in agriculture, defense, and search-and-rescue operations. However, these environments are inherently unpredictable and navigation remains a formidable challenge given the complex environmental conditions including challenging terrain and varying illumination. Off-road autonomy requires robust perception across multiple spatial scales and imaging modalities, yet most sensing setups struggle to capture both far-field environmental context and near-field terrain detail simultaneously across various illumination conditions. To facilitate development of robust off-road perception and traversability estimation systems, we propose MS-DOTT (Multi-Sensor Dataset for Off-road Terrain Traversability) a comprehensive multi-modal off-road dataset with long-range 3D sensing and short-range, fine-grained perception to facilitate high-fidelity terrain understanding by combining proprioceptive signals for tracking vehicle dynamics with exteroceptive imaging and pointcloud data. The dataset includes trajectories recorded across nominal lighting, low-light, and no-light conditions with diverse 2D and 3D sensing for both long-range large-scale and short-range high-resolution sensing, as well as 4D RADAR measurements that provide velocity estimates for detected pointclouds. We demonstrate the utility of the dataset by adapting a SOTA multi-sensor fusion method for the purpose of traversability estimation. This dataset will aid the robotics community in advancing research and development of methods for traversability estimation, off-road perception and multi-sensor fusion in unstructured environments.