George Mason University Dataverse

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21 to 30 of 3,563 Results

20250623_24min_45x45x30cm_ses2.mat Feb 18, 2026 - Tiling of large-scaled environments by grid cells requires experience MATLAB Data - 190.1 MB - MD5: 681d643296e780a5ed5a2f5621cf9302 limon = mouse5
20250623_46min_75x75x20cm.mat Feb 18, 2026 - Tiling of large-scaled environments by grid cells requires experience MATLAB Data - 373.4 MB - MD5: c83c683951284753237c3f330970171c limon = mouse5
20250625_26min_45x45x30cm_ses2.mat Feb 18, 2026 - Tiling of large-scaled environments by grid cells requires experience MATLAB Data - 462.5 MB - MD5: c876b5db0b80027db1d5dc07d723f863 reno = mouse6
20250625_35min_45x45x30cm.mat Feb 18, 2026 - Tiling of large-scaled environments by grid cells requires experience MATLAB Data - 593.3 MB - MD5: 67d8d68ec59776a35c87b95452c83a3f reno = mouse6
20250625_55min_75x75x40cm.mat Feb 18, 2026 - Tiling of large-scaled environments by grid cells requires experience MATLAB Data - 989.0 MB - MD5: b67c71e431fedbabe0310fa9c3660e88 reno = mouse6
20250626_24min_45x45x30cm.mat Feb 18, 2026 - Tiling of large-scaled environments by grid cells requires experience MATLAB Data - 431.1 MB - MD5: fa12fe74af304ed0cc1b85cfc92a5be7 limon = mouse5
20250626_30min_45x45x30cm_ses2.mat Feb 18, 2026 - Tiling of large-scaled environments by grid cells requires experience MATLAB Data - 113.8 MB - MD5: 4e954e0b841c68a536cab7ff0628824b limon = mouse5
20250626_50min_75x75x40cm.mat Feb 18, 2026 - Tiling of large-scaled environments by grid cells requires experience MATLAB Data - 265.2 MB - MD5: c4e8c81d6f92e4045924164bf3d54cd7 limon = mouse5
Read_me_dataverse_dataset_v2.txt Feb 18, 2026 - Tiling of large-scaled environments by grid cells requires experience Plain Text - 1.7 KB - MD5: a147ac8105c50497c8053a1a5cd86ba8 Read_me
Multi-Modal Passive Perception Dataset for Off-Road Mobility in Extreme Low-Light Conditions Jan 19, 2026 Xiao, Xuesu, 2024, "Multi-Modal Passive Perception Dataset for Off-Road Mobility in Extreme Low-Light Conditions", https://doi.org/10.13021/ORC2020/SP577T, George Mason University Dataverse, V4 This dataset contains multi-modal passive perception data, including thermal, event, and stereo RGB cameras, IMU, GPS, and LiDAR for ground truth, to enable off-road mobility in extreme low-light conditions.

20250623_24min_45x45x30cm_ses2.mat

Feb 18, 2026 - Tiling of large-scaled environments by grid cells requires experience

MATLAB Data - 190.1 MB -

limon = mouse5

20250623_46min_75x75x20cm.mat

Feb 18, 2026 - Tiling of large-scaled environments by grid cells requires experience

MATLAB Data - 373.4 MB -

limon = mouse5

20250625_26min_45x45x30cm_ses2.mat

Feb 18, 2026 - Tiling of large-scaled environments by grid cells requires experience

MATLAB Data - 462.5 MB -

reno = mouse6

20250625_35min_45x45x30cm.mat

Feb 18, 2026 - Tiling of large-scaled environments by grid cells requires experience

MATLAB Data - 593.3 MB -

reno = mouse6

20250625_55min_75x75x40cm.mat

Feb 18, 2026 - Tiling of large-scaled environments by grid cells requires experience

MATLAB Data - 989.0 MB -

reno = mouse6

20250626_24min_45x45x30cm.mat

Feb 18, 2026 - Tiling of large-scaled environments by grid cells requires experience

MATLAB Data - 431.1 MB -

limon = mouse5

20250626_30min_45x45x30cm_ses2.mat

Feb 18, 2026 - Tiling of large-scaled environments by grid cells requires experience

MATLAB Data - 113.8 MB -

limon = mouse5

20250626_50min_75x75x40cm.mat

Feb 18, 2026 - Tiling of large-scaled environments by grid cells requires experience

MATLAB Data - 265.2 MB -

limon = mouse5

Read_me_dataverse_dataset_v2.txt

Feb 18, 2026 - Tiling of large-scaled environments by grid cells requires experience

Plain Text - 1.7 KB -

Read_me

Multi-Modal Passive Perception Dataset for Off-Road Mobility in Extreme Low-Light Conditions

Jan 19, 2026

Xiao, Xuesu, 2024, "Multi-Modal Passive Perception Dataset for Off-Road Mobility in Extreme Low-Light Conditions", https://doi.org/10.13021/ORC2020/SP577T, George Mason University Dataverse, V4

This dataset contains multi-modal passive perception data, including thermal, event, and stereo RGB cameras, IMU, GPS, and LiDAR for ground truth, to enable off-road mobility in extreme low-light conditions.

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