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{"timestamp": 1765058977.638246, "position": [16.32521432506625, -10.909358896296363, 1.2122910095566657], "orientation": [0.06253080270102593, -0.13438205735681616, 0.2380896440699046, 0.9598670130600284]}
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{"timestamp": 1765058978.038222, "position": [16.936989609406833, -10.684217394222486, 1.3829451947649871], "orientation": [0.048800305144462576, -0.1252143030607074, 0.2820238077239172, 0.9499486725101159]}
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{"timestamp": 1765058978.1715522, "position": [17.143341234404804, -10.558133062640817, 1.439610398531343], "orientation": [0.058901699304129966, -0.15190007148901427, 0.26752042133639464, 0.9496787784660222]}
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{"timestamp": 1765058979.5715232, "position": [18.732713933002476, -9.750643326487438, 1.8871117634468386], "orientation": [0.08721419039390746, -0.13119084428764938, 0.3358878621060797, 0.9286344767769696]}
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{"timestamp": 1765058980.238769, "position": [19.262815884868886, -9.07510078245946, 2.0515112347056026], "orientation": [-0.015432062956628164, -0.14954607336917963, 0.5959295901129437, 0.7888382261278805]}
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{"timestamp": 1765058980.9048142, "position": [19.349200784499487, -8.263178772629937, 2.2315835897733125], "orientation": [-0.05524236425747189, -0.09891067528046403, 0.782277072923383, 0.6125418685161285]}
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{"timestamp": 1765058981.038172, "position": [19.335580571508498, -8.062196388458217, 2.2538960327407733], "orientation": [-0.05608016264994688, -0.11332034432054366, 0.8139397037481129, 0.5670235211899604]}
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{"timestamp": 1765058983.3047411, "position": [18.140076451118258, -5.105262667876645, 2.565570658104528], "orientation": [-0.045676613253830683, -0.05267063637023108, 0.8797613253440545, 0.47027594186274013]}
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{"timestamp": 1765058983.5048192, "position": [18.038431310102567, -4.866671180973805, 2.599875189397369], "orientation": [-0.05609931986885432, -0.03397371782488171, 0.8761489147279387, 0.47755809283108414]}
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{"timestamp": 1765058983.6385002, "position": [17.936843645524938, -4.687926222240535, 2.593830810911959], "orientation": [-0.06287969661659333, -0.05948181908522635, 0.8769716298150648, 0.47268257578551187]}
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{"timestamp": 1765058984.371366, "position": [17.484882483785622, -3.756873713915095, 2.699839493069369], "orientation": [-0.0786725808569514, -0.1119663292897453, 0.8700930397209918, 0.47351057892698767]}
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{"timestamp": 1765058984.5714262, "position": [17.357837633347412, -3.5018162803539976, 2.7134249870688567], "orientation": [-0.040632728217051536, -0.051437090049301526, 0.885404223388331, 0.4601766708243753]}
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{"timestamp": 1765058984.838037, "position": [17.20681587426092, -3.147181306053621, 2.70707118394869], "orientation": [-0.025500961990427916, -0.06137470663532184, 0.8728032355403226, 0.48352596450794133]}
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{"timestamp": 1765058985.371371, "position": [16.878863083508087, -2.4698115898555497, 2.758474791066999], "orientation": [-0.034701072196324866, -0.061066703911476804, 0.8708489789217223, 0.48650667947398935]}
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{"timestamp": 1765058987.1046622, "position": [15.737066439803106, -0.28114112308807415, 2.9038600776895147], "orientation": [-0.057416748071812324, -0.05892387572665043, 0.8660682611692403, 0.49310958305988817]}
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{"timestamp": 1765058989.3046012, "position": [14.127515965403878, 2.2025469839778165, 2.997597159379839], "orientation": [-0.0915984539932456, -0.0917248360042119, 0.9250105442832993, 0.3571439074809509]}
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{"timestamp": 1765058989.504605, "position": [14.069405447092327, 2.4297840212804775, 3.040170397037581], "orientation": [-0.09090451161541893, -0.09709915873573824, 0.8738122866590539, 0.46771808904985657]}
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Omni Instrument CV Project Dataset
The Omni Instrument CV Project is a compact robotics dataset combining:
- Raw ROS 2 MCAP bag (stereo, depth, TF, odom)
- Ground truth mesh (STL)
- Distilled synchronized dataset (100 samples)
Contents
1. Raw Data
Contains a stripped ROS 2 MCAP recording:
/zed/zedxm/left/color/rect/image/zed/zedxm/right/color/rect/image/zed/zedxm/depth/depth_registered/zed/zedxm/odom/tf,/tf_static- Associated
CameraInfo
2. Meshes
Includes:
- omni_mesh.stl — ground-truth geometry for TSDF + reconstruction metrics.
3. HuggingFace Sample
Load a sample and decode its metadata:
from datasets import load_dataset
import json
ds = load_dataset("OmniInstrument/CV_project")
sample = ds[0]
meta = json.loads(sample["metadata"])
timestamp = meta["timestamp"]
position = meta["position"] # [x, y, z]
orientation = meta["orientation"] # [qx, qy, qz, qw]
print("Timestamp:", timestamp)
print("Position:", position)
print("Orientation:", orientation)
License
This software and dataset are released under the MIT License.
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