|Related Video preview||https://www.youtube.com/channel/UCOU9e7xxqmL_s4QX6jsGZSw|
|Related Data Paper||Martin Miller, Soon-Jo Chung, and Seth Hutchinson. The Visual–Inertial Canoe Dataset. The International Journal of Robotics Research, 37(1):13--20, 2018.|
|Related Thesis||Martin Miller. "Hardware and Software Considerations for Monocular SLAM in a Riverine Environment." Master's thesis, University of Illinois at Urbana-Champaign, 2017.|
If you use this dataset, please cite the IJRR data paper (bibtex is below).
We present a dataset collected from a canoe along the Sangamon River in Illinois. The canoe was equipped with a stereo camera, an IMU, and a GPS device, which provide visual data suitable for stereo or monocular applications, inertial measurements, and position data for ground truth. We recorded a canoe trip up and down the river for 44 minutes covering 2.7 km round trip. The dataset adds to those previously recorded in unstructured environments and is unique in that it is recorded on a river, which provides its own set of challenges and constraints that are described
Video previews are available on Youtube:
The information below can also be found in the README files provided in the 527 dataset and each of its subsets. The purpose of this document is to assist researchers in using this dataset.
R0: The rectifying rotation matrix of the left camera.
T_cam_imu: Transformation matrix for a point in the IMU frame to the camera frame.
distortion_coeffs: lens distortion coefficients using the radial tangential model.
intrinsics: focal length x, focal length y, principal point x, principal point y
resolution: resolution of calibration. Scale the intrinsics for use with the raw 800x600 images. The distortion coefficients do not change when the image is scaled.
T_cn_cnm1: Transformation matrix from the right camera to the left camera.
unused: The first column is all 1s and can be ignored.
software frame number: This number increments at the end of every iteration of the software loop.
camera frame number: This number is generated by the camera and increments each time the shutter is triggered. The software and camera frame numbers do not have to start at the same value, but if the difference between the initial and final values is not the same, it suggests that frames may have been dropped.
camera timestamp: This is the cameras internal timestamp of the frame capture in units of 100 milliseconds.
PC timestamp: This is the PC time of arrival of the image.
|Subject||Technology and Engineering|
|Funder||Office of Naval Research - Grant: N00014-14-1-0265|
|Corresponding Creator||Seth Hutchinson|