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The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    UnidentifiedImageError
Message:      cannot identify image file <_io.BytesIO object at 0x7fa730108d60>
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
                  return get_rows(
                         ^^^^^^^^^
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                         ^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/utils.py", line 77, in get_rows
                  rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2543, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2061, in __iter__
                  batch = formatter.format_batch(pa_table)
                          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/formatting/formatting.py", line 472, in format_batch
                  batch = self.python_features_decoder.decode_batch(batch)
                          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/formatting/formatting.py", line 234, in decode_batch
                  return self.features.decode_batch(batch, token_per_repo_id=self.token_per_repo_id) if self.features else batch
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 2161, in decode_batch
                  decode_nested_example(self[column_name], value, token_per_repo_id=token_per_repo_id)
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1419, in decode_nested_example
                  return schema.decode_example(obj, token_per_repo_id=token_per_repo_id) if obj is not None else None
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/image.py", line 190, in decode_example
                  image = PIL.Image.open(bytes_)
                          ^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/PIL/Image.py", line 3498, in open
                  raise UnidentifiedImageError(msg)
              PIL.UnidentifiedImageError: cannot identify image file <_io.BytesIO object at 0x7fa730108d60>

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SPICE-HL3: Single-Photon, Inertial, and Stereo Camera dataset for Exploration of High-Latitude Lunar Landscapes

Paper | Code | Data (Zenodo)

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This dataset presents data recorded at the LunaLab from the Interdisciplinary Research Center for Security reliability and Trust (SnT) at the University of Luxembourg, an indoor test facility designed to replicate the optical characteristics of multiple lunar latitudes. The data recorded is specifically intended for engineers and researches working on vision-based robotic navigation (and associated tools) across high-latitude lunar regions; although it could also serve those working on computer vision applications at the edges of the spectrum of perceptually degraded scenarios. It includes images, inertial, and wheel odometry data from robots navigating seven distinct trajectories under multiple high-latitude illumination scenarios, simulating conditions from lunar dawn to night time with and without the aid of headlights. Data was captured using Leo rovers incorporating a ZED2 stereo-inertial sensor, a FLIR Blackfly-S monocular monochrome camera, and a novel single-photon avalanche diode (SPAD) camera. We recorded both static and dynamic image sequences, with robots navigating at slow (5 cm/s) and fast (50 cm/s) speeds. All data is calibrated, synchronized, and timestamped, providing a valuable resource for validating vision-based autonomous navigation pipelines for future lunar missions targeting high-latitude regions.

Citation

Rodríguez-Martínez, D., van der Meer, D., Song, J., Bera, A., Pérez del Pulgar, C., & Olivares-Mendez, M. A. (2026). SPICE-HL3: Single-Photon, Inertial, and Stereo Camera dataset for Exploration of High-Latitude Lunar Landscapes. Scientific Data. https://doi.org/10.1038/s41597-026-06668-8

@article{rodriguez2025spicehl3,
  title={{SPICE}-{HL3}: Single-Photon, Inertial, and Stereo Camera dataset for Exploration of High-Latitude Lunar Landscapes},
  author = {Rodríguez-Martínez, David and van der Meer, Dave and Song, Junlin and Bera, Abishek and Pérez del Pulgar, C.J. and Olivares-Mendez, Miguel Angel},
  journal={Scientific Data},
  volume={},
  number={},
  pages={},
  doi = {10.1038/s41597-026-06668-8},
  url = {https://www.nature.com/articles/s41597-026-06668-8},
  year = {2026}
}

Leaderboard

This is a public leaderboard showcasing the performance of various Visual Odometry and SLAM methods evaluated on different trajectories from the SPICE-HL3 dataset.

Fast Sequences SPICE-HL3 Leaderboard

Rank Method Sensor Trj_A Trj_B Trj_C Trj_D Trj_E Trj_F Trj_G avATE RMSE [cm] avATE Max [cm]
1 🔥 Wheel Odometry WODO 34.66 (63.91) 124.87 (213.86) 85.40 (123.33) 140.70 442.36
2 RTAB-Map Stereo 64.77 (97.44) 29.12 (35.29) 64.50 (95.65) 145.56 (202.12) 253.50 965.61
3 ORB-SLAM3 Stereo 83.72 (135.86) 83.23 (135.47) 17.31 (31.72) 841.15 4161.01
4 ORB-SLAM3 Mono 100.27 (168.82) 94.04 (159.92) 859.51 4247.57
5 Inertial Odometry (naive) IMU 473.97 (1183.15) 928.11 (2341.06) 816.84 (2384.15) 938.21 4265.62

➖: Denotes trajectories that have not been evaluated. ❌ : Describe trajectories in which localization is lost and unrecovered; the method fails to provide a final estimate.

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