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1633714101.62 1.751798445326358e-05 -7.052295741161174e-06 2.373457196699902e-05 2.8654222909235758e-05 9.097387245721107e-06 1.3005783968756288e-05 0.9999999994635113
1633714101.62 4.469599291789805e-06 -9.000369444289724e-06 2.2679623353172307e-05 8.65828737754814e-06 -0.00010269552123353556 1.880626095400435e-05 0.9999999945124944
1633714101.63 4.984065100632762e-07 -2.1545475313855533e-06 1.868162893706171e-05 2.026756384394471e-05 -5.661885380728183e-05 -4.429184777982036e-05 0.9999999972108817
1633714101.63 2.544783708868792e-06 -1.50198202109042e-06 8.210716935602713e-06 2.7820181018266208e-05 -0.00010871944990991023 -2.033407501587327e-05 0.9999999934963222
1633714101.64 5.417440326560552e-06 6.81017301673581e-06 5.740259468062225e-06 -4.092020364407842e-06 -8.022856389576598e-05 6.713330579997355e-06 0.9999999967507821
1633714101.64 1.1919817349825834e-05 -7.789426279769529e-06 1.2006908108985748e-05 1.028715228968782e-05 -2.9212600177133558e-05 2.1652593994538362e-05 0.9999999992859819
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Minimal Texture Dataset for RGB-D SLAM

RGB-D sequences recorded with an Intel RealSense D435i over low-texture conceptual-geometry and sand scenes at DLR (German Aerospace Center), with millimeter-accurate ground truth from a Vicon motion-capture system. The dataset was introduced for the evaluation of SID-SLAM: Semi-Direct Information-Driven RGB-D SLAM (IEEE RA-L, 2023).

This repository is the authoritative re-upload of the dataset previously published on Zenodo (10.5281/zenodo.10453700), regenerated from the original recordings. It extends the published set with the full first recording session and the previously unpublished easy sequences.

Contents

Sequences are grouped by recording day:

  • 10-08/ — first session, 2021-10-08 (15 sequences: parallel_lines, circles, hexagon, triangle, deformation_model, sand, square_with_circles, and loop variants)
  • 10-14/ — second session, 2021-10-14 (18 sequences: the 14 published on Zenodo — triangle, circle, square, hexagon, dodecagon, lines, the three circle_* loop sequences, sand_rocks — plus forward_backward_01, circle_only_01, lru_01 and ardea_01)

Each sequence ships two files:

  • <day>/<name>.bag — ROS1 bag (bz2-compressed) with the RealSense D435i streams:
    • /camera/color/image_raw — rgb8, 1280x720 @ 30 Hz
    • /camera/aligned_depth_to_color/image_raw — 16UC1 depth aligned to color, millimeters, 1280x720 @ 30 Hz
    • /camera/color/camera_info, /camera/aligned_depth_to_color/camera_info — intrinsics
    • /camera/imu — ~200 Hz (camera-to-IMU extrinsics not included)
    • /camera/extrinsics/depth_to_color
  • <day>/<name>_groundtruth.txt — Vicon ground-truth trajectory in TUM format (timestamp[s] tx ty tz qx qy qz qw), evaluation-ready, same clock as the bag.

T_vicon_camera.txt (repo root) holds the Vicon-marker to camera_color_optical_frame extrinsic.

Calibration

Color intrinsics (identical for both sessions, zero distortion / plumb_bob, 1280x720):

fx = 908.479248  fy = 907.841919
cx = 650.889160  cy = 364.692657

The aligned depth stream shares these intrinsics by construction. Depth in meters = value / 1000.

Calibration recordings & raw metadata (archive)

The original calibration recordings and per-sequence provenance from the 2021 sessions are archived here as well (not needed to run the sequences, but preserved as the primary copy):

  • calibration/10-08/timestamps_camera_calibration_seq01.bag, viconMarker_camera_calibration_seq01.bag, viconMarker_camera_calibration_seq02.bag
  • calibration/10-14/deformation_model_calibration.bag, lukas_pattern_calibration_01.bag, photo_calibration.bag

These are the bz2-compressed ROS1 bags used for the Vicon-marker/camera extrinsic (T_vicon_camera.txt), camera-clock/Vicon timestamp calibration, and photometric/pattern calibration.

  • metadata/10-08.tar.gz, metadata/10-14.tar.gz — the original per-sequence recording folders (environment and git-version snapshots, params.yaml, tf.cfg, raw Vicon/tf bags, and vicon_direct_features_*.txt), documenting how each sequence's ground truth was produced.

Citation

@article{fontan2023sid,
  title={{SID-SLAM}: Semi-Direct Information-Driven {RGB-D SLAM}},
  author={Font{\'a}n, Alejandro and Giubilato, Riccardo and Oliva, Laura and Civera, Javier and Triebel, Rudolph},
  journal={IEEE Robotics and Automation Letters},
  volume={8},
  number={10},
  pages={6387--6394},
  year={2023}
}

License

CC BY 4.0

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