Handheld

RoNIN

Learned pedestrian inertial navigation and body heading

Smartphone inertial sequences paired with body-mounted Tango trajectories and calibration for natural phone handling and pedestrian navigation.

Metadata: Complete Metadata source documented Released 2019

Quick facts

Vehicle type
Handheld
Environment
Indoor
Data origin
Real-world
Sensor count
1
Ground truth
Available
Calibration
Parameters Only
Annotations
Not reported

Sensor overview

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

Ground truth

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

Ground-truth references for RoNIN
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.
3D position + heading Visual SLAM Asus Zenfone AR / Tango Area Learning Not reported 200 Hz 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
Accuracy notes
Below 0.3 m return-to-reference drift over ten minutes; not a uniform absolute-position accuracy guarantee.
Reference notes
Body/harness reference rather than freely handled phone position. Tango relocalized/incremental trajectories are manually combined; heading assumes a constant harness offset. The loader reads Tango poses and synchronized timestamps.

Calibration and synchronization

Reported calibration level, reproducibility signals, and supporting notes.

Level
Parameters Only
Processed parameters
Available
Raw calibration data
Not reported
Calibration targets
Not reported

Parameters only

Processed calibration parameters are reported, but raw calibration data is not reported.

Notes

Per-sequence inertial bias/scale and cross-phone spatial/time alignment.

Known or intentional limitations

Reported constraints and characteristics to check before using the dataset.

The maintained repository says approximately half of the paper collection was not published. Paper totals of 276 sequences, 42.7 hours and 100 subjects do not describe the verified public download inventory.

The reference tracks a harness-mounted body device, not the freely handled capture phone. Visual tracking and manual relocalization correction do not establish independent absolute-position truth.

Alternative phone IMUs are not simultaneous devices. The 200 Hz synchronized sample grid does not establish separate native accelerometer/gyroscope rates.

Training-only filtered orientation can reuse reference trajectories; test-time Android orientation has different dependencies. Return-to-reference drift is not a uniform absolute-accuracy bound.

The official loader establishes inertial, Tango-pose and calibration fields. Public pressure/magnetometer arrays and raw tracking imagery were not established; they are omitted from sensor inventory.

Project/DOI requests timed out during review. The author-linked archive remains listed as the download destination; archive contents, exact public totals and achieved alignment error remain unverified.

Cameras

Not reported

IMUs

Capture-phone IMU

Model
Not reported
Count
1
Accel / gyro
Not reported

GNSS

Not reported

LiDAR

Not reported

Radar

Not reported

Additional sensors

Not reported

Annotations

Reported annotation availability and task support.

Availability
Not reported
Format
Not reported
Class count
Not reported
Semantic segmentation Not reported
Instance segmentation Not reported
Object detection Not reported
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
yan2019ronin

BibTeX

@misc{yan2019ronin,
  author = {Yan, Hang and Herath, Sachini and Furukawa, Yasutaka},
  title = {RoNIN: Robust Neural Inertial Navigation in the Wild: Benchmark, Evaluations, and New Methods},
  year = {2019},
  eprint = {1905.12853},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/1905.12853v1}
}

Dataset corrections

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