MS-DOTT: A Multi-Modal Dataset for Off-Road Traversability Estimation
Overview
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.
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Dataset · modalities: proprioceptive signals · Differing source assertions
“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”
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Dataset · modalities: exteroceptive imaging · Differing source assertions
“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”
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Dataset · tasks: traversability estimation
“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”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalMS-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.
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