Calibration of Extrinsic Transformation Using Manifold Optimization

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

Data fusion with multiple heterogeneous sensors has shown great importance for motion control and navigation filter design of autonomous vehicles. However, data fusion often
requires the prior knowledge of the extrinsic transformations between sensors. This paper focuses on the usage of manifold optimization to calibrate the extrinsic transformation for a pair of sensors from a batch of measurements. Instead of reparameterization the transformation matrix in other forms, we formulate an objective function directly with the special Euclidean group.
Then this manifold optimization problem is solved iteratively in a Gauss-Newton fashion. The usage of manifold optimization guarantees the obtained result being strictly within the special
Euclidean group without the need of further operations like normalization and orthogonalization. The experimental results on both synthetic and real data show the superiority and robustness of the proposed method. Considering the performance and time consumption, the proposed approach is a good option for real applications.
Original languageEnglish
Book seriesI F A C Workshop Series
Volume52
Issue number8
Pages (from-to)124-129
ISSN1474-6670
DOIs
Publication statusPublished - 2019
Event10th IFAC Symposium on Intelligent Autonomous Vehicles (IAV 2019) - Gdansk, Poland
Duration: 3 Jul 20195 Jul 2019
Conference number: 10

Conference

Conference10th IFAC Symposium on Intelligent Autonomous Vehicles (IAV 2019)
Number10
CountryPoland
CityGdansk
Period03/07/201905/07/2019

Keywords

  • Calibration
  • Manifold Optimisation
  • Sensor Fusion
  • Multisensor Integration

Cite this

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title = "Calibration of Extrinsic Transformation Using Manifold Optimization",
abstract = "Data fusion with multiple heterogeneous sensors has shown great importance for motion control and navigation filter design of autonomous vehicles. However, data fusion oftenrequires the prior knowledge of the extrinsic transformations between sensors. This paper focuses on the usage of manifold optimization to calibrate the extrinsic transformation for a pair of sensors from a batch of measurements. Instead of reparameterization the transformation matrix in other forms, we formulate an objective function directly with the special Euclidean group.Then this manifold optimization problem is solved iteratively in a Gauss-Newton fashion. The usage of manifold optimization guarantees the obtained result being strictly within the special Euclidean group without the need of further operations like normalization and orthogonalization. The experimental results on both synthetic and real data show the superiority and robustness of the proposed method. Considering the performance and time consumption, the proposed approach is a good option for real applications.",
keywords = "Calibration, Manifold Optimisation, Sensor Fusion, Multisensor Integration",
author = "Xiao Hu and Daniel Olesen and Per Knudsen",
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language = "English",
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}

Calibration of Extrinsic Transformation Using Manifold Optimization. / Hu, Xiao; Olesen, Daniel; Knudsen, Per.

In: I F A C Workshop Series, Vol. 52, No. 8, 2019, p. 124-129.

Research output: Contribution to journalJournal articleResearchpeer-review

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AU - Hu, Xiao

AU - Olesen, Daniel

AU - Knudsen, Per

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AB - Data fusion with multiple heterogeneous sensors has shown great importance for motion control and navigation filter design of autonomous vehicles. However, data fusion oftenrequires the prior knowledge of the extrinsic transformations between sensors. This paper focuses on the usage of manifold optimization to calibrate the extrinsic transformation for a pair of sensors from a batch of measurements. Instead of reparameterization the transformation matrix in other forms, we formulate an objective function directly with the special Euclidean group.Then this manifold optimization problem is solved iteratively in a Gauss-Newton fashion. The usage of manifold optimization guarantees the obtained result being strictly within the special Euclidean group without the need of further operations like normalization and orthogonalization. The experimental results on both synthetic and real data show the superiority and robustness of the proposed method. Considering the performance and time consumption, the proposed approach is a good option for real applications.

KW - Calibration

KW - Manifold Optimisation

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