Name
Abstract | ||
|---|---|---|
We present a dataset for evaluating the performance of visual-inertial odometry (VIO) systems employing an onboard light source. The dataset consists of 39 sequences, recorded in mines, tunnels, and other dark environments, totaling more than 160 minutes of stereo camera video and IMU data. In each sequence, the scene is illuminated by an onboard light of approximately 1300, 4500, or 9000 lumens. We accommodate both direct and indirect visual odometry methods by providing the geometric and photometric camera calibrations (i.e. response, attenuation, and exposure times). In contrast with existing datasets, we also calibrate the light source itself and publish data for inferring more complex light models. Ground-truth position data are available for a subset of sequences, as captured by a Leica total station. All remaining sequences start and end at the same position, permitting the use of total accumulated drift as a metric for evaluation. Using our proposed benchmark, we analyze the performance of several start-of-the-art VO and VIO frameworks. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/IROS40897.2019.8968554 | 2019 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS) |
Field | DocType | ISSN |
Computer vision,Stereo camera,Visual odometry,Computer science,Odometry,Photometry (optics),Artificial intelligence,Inertial measurement unit,Attenuation,Total station,Calibration | Conference | 2153-0858 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mike Kasper | 1 | 2 | 1.38 |
| Steve McGuire | 2 | 4 | 1.78 |
| Christoffer R. Heckman | 3 | 12 | 10.78 |