ICRA 2020 FPV Drone Racing VIO Competition
A second competition using this dataset was held for ICRA 2020. The goal was to estimate the quadrotor motion as accurately as possible, utilizing any desired sensor combinations, and to improve upon the performance of previous year’s submissions. Unfortunately, none of the submissions outperformed last year’s best submission. The competition was hosted on this page.
Results
- Evaluation: The relative pose errors at the sub-trajectory of lengths {40, 60, 80, 100, 120} meters are computed. The average translation and rotation error over all sequences are used for ranking.
- Naming rule: The name in the following table is the combinition of the initials of the participant’s last name and the affliation.
- Sensor coding: S – sensors from the Snapdragon board; D – sensors from the DAVIS.
- References: References (e.g., report) are available upon the participants’ consent.
| Ranking | Name | Sensors | Translation (%) | Rotation (deg/m) | References |
|---|---|---|---|---|---|
| 1 | OKVIS 2.0 | stereo (S) + inertial | 7.148 | 0.262 | report |
| Dr. Stefan Leutenegger is leading the Smart Robotics Lab (SRL) at Imperial College, London. | |||||
| 2 | OpenVINS | mono (S) + inertial | 7.198 | 0.267 | report ; code |
| Patrick Geneva is part of the Robot Perception and Navigation Group (RPNG) at the University of Delaware. | |||||
| 3 | OSU-ETHZ | stereo (S) + inertial | 7.277 | 0.266 | report |
| Dr. Jianzhu Huai is with the SPIN lab at The Ohio State University. | |||||
IROS 2019 FPV Drone Racing VIO Competition
A first competition using this dataset was held jointly with IROS 2019 Workshop “Challenges in Vision-based Drone Navigation” on November 8, 2019 in Macau. The goal was to estimate the quadrotor motion as accurately as possible, utilizing any desired sensor combinations. The winner was awarded 1,000 USD and invited to present their approach at the workshop. The competition was hosted on this page.
Results
- Evaluation: The relative pose errors at the sub-trajectory of lengths {40, 60, 80, 100, 120} meters are computed. The average translation and rotation error over all sequences are used for ranking.
- Naming rule: The name in the following table is the combinition of the initials of the participant’s last name and the affliation.
- Sensor coding: S – sensors from the Snapdragon board; D – sensors from the DAVIS.
- References: References (e.g., report) are available upon the participants’ consent.
| Ranking | Name | Sensors | Translation (%) | Rotation (deg/m) | References |
|---|---|---|---|---|---|
| 1 | g-d | binocular (S) + inertial | 7.023 | 0.264 | report ; code (OpenVINS) |
| Patrick Geneva is part of the Robot Perception and Navigation Group (RPNG) at the University of Delaware. | |||||
| 2 | m-l | mono (S) + inertial | 7.034 | 0.266 | report |
| Thomas Mörwald is with Leica Geosystems. | |||||
| 3 | u-t | stereo (S) + inertial | 7.778 | 0.285 | report ; code (Basalt) |
| Vladyslav Usenko is with the Computer Vision Group at the Technical University of Munich. | |||||
| 4 | a-u * | stereo (S) + inertial | 11.869 | 0.619 | code |
| 5 | r-u | stereo (S) + inertial | 36.048 | 1.894 | – |