Title | ||
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Build your own visual-inertial odometry aided cost-effective and open-source autonomous drone. |
Abstract | ||
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This paper describes an approach to building a cost-effective and research grade visual-inertial odometry aided vertical taking-off and landing (VTOL) platform. We utilize an off-the-shelf visual-inertial sensor, an onboard computer, and a quadrotor platform that are factory-calibrated and mass-produced, thereby sharing similar hardware and sensor specifications (e.g., mass, dimensions, intrinsic and extrinsic of camera-IMU systems, and signal-to-noise ratio). We then perform a system calibration and identification enabling the use of our visual-inertial odometry, multi-sensor fusion, and model predictive control frameworks with the off-the-shelf products. This implies that we can partially avoid tedious parameter tuning procedures for building a full system. The complete system is extensively evaluated both indoors using a motion capture system and outdoors using a laser tracker while performing hover and step responses, and trajectory following tasks in the presence of external wind disturbances. We achieve root-mean-square (RMS) pose errors between a reference and actual trajectories of 0.036m, while performing hover. We also conduct relatively long distance flight (~180m) experiments on a farm site and achieve 0.82% drift error of the total distance flight. This paper conveys the insights we acquired about the platform and sensor module and returns to the community as open-source code with tutorial documentation. |
Year | Venue | Field |
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2017 | arXiv: Robotics | Inertial frame of reference,Motion capture,Simulation,Model predictive control,Odometry,Control engineering,Drone,Engineering,Laser tracker,Trajectory,Calibration |
DocType | Volume | Citations |
Journal | abs/1708.06652 | 1 |
PageRank | References | Authors |
0.37 | 10 | 8 |
Name | Order | Citations | PageRank |
---|---|---|---|
In-kyu Sa | 1 | 186 | 18.55 |
mina kamel | 2 | 78 | 11.98 |
M. Burri | 3 | 343 | 18.62 |
Michael Blösch | 4 | 427 | 31.24 |
Raghav Khanna | 5 | 30 | 5.12 |
Marija Popovic | 6 | 11 | 1.68 |
Juan I. Nieto | 7 | 939 | 88.52 |
Roland Siegwart | 8 | 7640 | 551.49 |