Yang Zhao
ABSTRACT
Robust 3D pose tracking of an object is a critical technique for various mobile sensing applications. Computer vision-based pose tracking method provides a cost-effective solution, but it is sensitive to occlusion and illumination change issues. In this work, we propose a novel visual-inertial sensor fusion framework and demonstrate the real-time implementation of a tightly-coupled sensor fusion algorithm: inertial perspective-n-point (IPNP) algorithm. With measurements from an inertial measurement unit (IMU), the prototype system only needs to detect two keypoints to track all six degrees of freedom of a planar object, e.g., a mobile X-ray detector, a 50% reduction on required number of keypoints, compared with the vision-based perspective-n-point algorithm.
- Raul Acuna and Volker Willert. 2018. Robustness of control point configurations for homography and planar pose estimation. CoRR abs/1803.03025 (2018).Google Scholar
- J. Delmerico and D. Scaramuzza. 2018. A Benchmark Comparison of Monocular Visual-Inertial Odometry Algorithms for Flying Robots. In 2018 IEEE ICRA.Google Scholar
- D. Scaramuzza and F. Fraundorfer. 2011. Visual Odometry [Tutorial]. IEEE Robotics Automation Magazine 18, 4 (Dec 2011), 80--92.Google ScholarCross Ref
Index Terms
Visual and inertial sensor fusion for mobile X-ray detector tracking: demo abstract
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