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Rutgers APC RGBD Dataset

Object detection and 6DOF pose estimation for warehouse picking

10,368 registered RGBD images of 24 APC target objects in shelf bins, with controlled clutter, viewpoints, repeated frames and manually aligned object poses.

Metadata: Complete Metadata needs review

Primary places to inspect, download, read about, or reproduce the dataset.

Download

Not reported

Code

Not reported

Supplementary

Not reported

Quick facts

Vehicle type
Other
Environment
Indoor
Data origin
Real-world
Sensor count
2
Ground truth
Available
Calibration
Not reported
Annotations
Available

Sensor overview

High-level sensor availability before the detailed sensor records below.

Cameras

2 reported

IMUs

Not reported

GNSS

Not reported

LiDAR

Not reported

Radar

Not reported

Pitot tube

Not reported

Magnetometers

Not reported

Barometers

Not reported

UWB

Not reported

Ground truth

Reference scope, method, rate, coverage, and provenance.

Scope Method Reference system Count Rate Coverage Reference provenance
External
Measured using equipment independent of the platform’s normal onboard sensor pipeline.
Derived
Computed primarily from the dataset’s own recorded sensors.
Hybrid
Combines independent reference equipment with onboard measurements.
6DoF Manual annotation Not reported Not reported Not reported Full Reference provenance
External
Measured using equipment independent of the platform’s normal onboard sensor pipeline.
Derived
Computed primarily from the dataset’s own recorded sensors.
Hybrid
Combines independent reference equipment with onboard measurements.
Derived
Reference notes
Static target-object pose, not a platform trajectory. Annotators translate and rotate object meshes in RViz over the recorded point cloud. Each image has YAML transforms between robot base, camera and target object.

Calibration and synchronization

Reported calibration level, reproducibility signals, and supporting notes.

Level
Not reported
Processed parameters
Not reported
Raw calibration data
Not reported
Calibration targets
Not reported

Calibration data

Calibration reproducibility details are not reported.

Notes

Per-image robot-base/camera transforms are supplied. The paper does not establish a reproducible intrinsic/extrinsic calibration release.

Known or intentional limitations

Reported constraints and characteristics to check before using the dataset.

Mead index cards are excluded from the 25-object APC set.

The dataset does not focus on jointly estimating occluding and occluded objects for rearrangement.

Cameras

kinect-rgbd

Model
Microsoft Kinect v1
Setup
Rgbd
Count
1
Effective cameras
2
Modality
Rgbd
Resolution
Not reported
Rate
Not reported
Shutter
Not Reported
Lens type
Not Reported
HFOV
Not reported
VFOV
Not reported
Notes
Mounted on the Motoman Dual-arm SDA10F end joint. Structured-light intensity changed for closer operation. Four stationary samples per viewpoint over several seconds; no numeric recording rate is established.

IMUs

Not reported

GNSS

Not reported

LiDAR

Not reported

Radar

Not reported

Pitot tube

Not reported

Additional sensors

Not reported

Annotations

Reported annotation availability and task support.

Availability
Available
Format
YAML
Class count
24
Semantic segmentation Not reported
Instance segmentation Not reported
Object detection Available
Optical flow Not reported
Depth ground truth Not reported

Citation

Citation key and BibTeX kept at the end of the page for reference.

Citation key
Not reported

BibTeX

Not reported

Dataset corrections

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