Home | Dataset/Code | Challenge

High-level summary

Data

1. Training data (train_synthetic/). You can access them when signing up for the challenge **here**

2. Example test data (test_synthetic/) where tracking loss segments were already chosen. The example is included in the

   **DIPr GitHub page.**

Code

1. Generates simulated tracking failures, creates initial conditions with an error model, and adds noise to simulated IMU data
2. Performs evaluation/analysis (by computing VMAE and comparing to pure IMU integration) and visualize results
3. Includes an Arcturus pre-trained model

Available at:

DIPr GitHub page

Data Details

Data format

We share our data as a set of hdf5 files (one file per recording). The DIPr GitHub page repo contains sample code for reading and using the data. Unpacked data has the format below :

1. A numpy arrays of shapes (N,7)
2. A numpy arrays of shapes (N, 32)

Helper classes were added in the code to access the data by name.

imu, states, segments = load_hdf5_data()

# time | gyro xyz | acc xyz | END
# 0      1          4         7
imu: np.ndarray # shape = (N_samples, 7)

# time | isometry 1x16 | vel xyz | w xyz | gb xyz | ab xyz | gravity xyz | END
# 0      1               17        20      23       26       29            32
states: np.ndarray # shape = (N_samples, 32)

# Test only: contaons N_segments of (beg_time, end_time) tuples in seconds
segmennts : np.ndarray # shaep = (N_segments, 2)

# where
# time, seconds
# gyro rad/s, acc m/s2 are measurements in IMU frame, acc is inverseOfBodyAccel convention
# isometry is a transform from IMU frame to world, 
# vel m/s, w rad/s are linear and angular velocities of IMU in world
# gb rad/s, ab m/s2 are gyro and accel biases, that are zeros for synthetic data
# gravity m/s2 is gravity in world and equal to [0, 9.81, 0] for synthetic dataset

This format is used in case we want to release live recorded tracker data in the future, which may have different biases and gravity estimates per frame.

Dataset Recording / Creation

The data in this release is synthetic only and was generated as follow :

• We recorded head-mounted VR headset trajectories taken while a user was in VR (playing games, watching Google Earth, etc). Those trajectories were recorded using a Lighthouse tracking system providing data at ~200Hz (could vary). Around a dozen people participated in this recording for data diversity purposes.
• We then fit B-Splines to the trajectories, which we use to generate synthetic IMU data and synthetic ground truth positions and velocities at 1000Hz. The synthetic IMU data is generated by estimating 1st rotational and 2nd translation derivative.

This release includes a total duration of about 6.54 hours of human interaction with VR applications. The provided model was trained on similar data.

It is worth mentioning that our synthetic IMU samples are perfect and have zero biases in contrast to IMU data recorded in a real-life setup. We don’t consider this a problem since live, with a SLAM tracker, we always have a current estimate for the biases which can be subtracted from the signal (up to bias estimation error).

Example Test Data Details

We provide a dataset named OpenVR_2021-09-02_17-40-34-synthetic.hdf5 inside the DIPr github page repo (’shared/test_synthetic’). It’s a synthetic dataset containing perfect IMU data, initial positions and velocities.