MS-DOTT: A Multi-Modal Dataset for Off-Road Traversability Estimation
MS-DOTT: A Multi-Modal Dataset for Off-Road Traversability Estimation
This paper introduces MS-DOTT (Multi-Sensor Dataset for Off-road Terrain Traversability), a comprehensive multi-modal dataset designed to advance off-road autonomy. It features trajectories recorded under nominal, low-light, and no-light conditions, incorporating diverse 2D and 3D sensing for both long-range and short-range perception, as well as 4D RADAR measurements for velocity estimation. The dataset integrates proprioceptive signals for vehicle dynamics tracking with exteroceptive imaging and pointcloud data to facilitate high-fidelity terrain understanding in unstructured environments.
The authors present MS-DOTT, a new multi-modal dataset designed to support off-road traversability estimation by combining long-range 3D sensing, short-range fine-grained perception, and 4D RADAR measurements across varying illumination conditions.
Source: arXiv Robotics — research abstracts · Read original article ↗
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What the source reports
Publisher-reported claims, with original evidence. These results have not been independently verified by RoboSignal.
Dataset details reported by the source
- Modalities
- proprioceptive signals Open source S4exteroceptive imaging Open source S4
- Tasks
- traversability estimation Open source S5
- paper: not_reported
- code: not_reported
- weights: not_reported
- dataset: not_reported
What remains unknown
Dataset licensing and commercial-use rights are not established here.
Source excerpts and review record
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y) 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 bo
Open source S4
th 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
Open source S5
-road perception and multi-sensor fusion in unstructured environments.
Open source S6
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Source：arXiv Robotics — research abstracts · arxiv.org