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Trust Your IMU: Con...
Trust Your IMU: Consequences of Ignoring the IMU Drift
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- Valtonen Örnhag, Marcus (författare)
- Centre for Mathematical Sciences, Lund University, Sweden
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- Persson, Patrik (författare)
- Centre for Mathematical Sciences, Lund University, Sweden
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- Wadenbäck, Mårten (författare)
- Linköpings universitet,Datorseende,Tekniska fakulteten
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- Åström, Kalle (författare)
- Centre for Mathematical Sciences, Lund University, Sweden
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- Heyden, Anders (författare)
- Centre for Mathematical Sciences, Lund University, Sweden
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(creator_code:org_t)
- IEEE Computer Society, 2022
- 2022
- Engelska.
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Ingår i: Proceedings 2022 IEEE/CVF Conference on Computer Visionand Pattern Recognition Workshops. - : IEEE Computer Society. - 9781665487399 - 9781665487405 ; , s. 4467-4476
- Relaterad länk:
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https://arxiv.org/ab...
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- In this paper, we argue that modern pre-integration methods for inertial measurement units (IMUs) are accurate enough to ignore the drift for short time intervals. This allows us to consider a simplified camera model, which in turn admits further intrinsic calibration. We develop the first-ever solver to jointly solve the relative pose problem with unknown and equal focal length and radial distortion profile while utilizing the IMU data. Furthermore, we show significant speed-up compared to state-of-the-art algorithms, with small or negligible loss in accuracy for partially calibrated setups.The proposed algorithms are tested on both synthetic and real data, where the latter is focused on navigation using unmanned aerial vehicles (UAVs). We evaluate the proposed solvers on different commercially available low-cost UAVs, and demonstrate that the novel assumption on IMU drift is feasible in real-life applications. The extended intrinsic auto-calibration enables us to use distorted input images, making tedious calibration processes obsolete, compared to current state-of-the-art methods. Code available at: https://github.com/marcusvaltonen/DronePoseLib.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datorseende och robotik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Vision and Robotics (hsv//eng)
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)
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