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Article

Continuous Fusion of Motion Data Using an Axis-Angle Rotation Representation with Uniform B-Spline †

1
Institut of Measurement and Control Systems, Karlsruhe Institute of Technology (KIT), Engler-Bunte-Ring 21, 76131 Karlsruhe, Germany
2
Atlatec GmbH, Haid-und-Neu-Straße 7, 76131 Karlsruhe, Germany
*
Author to whom correspondence should be addressed.
This paper is an extended version of our paper published in Hu, H.; Beck, J.; Lauer, M.; Stiller, C. Continuous Fusion of IMU and Pose Data using Uniform B-Spline. In Proceedings of the 2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), Karlsruhe, Germany, 14–16 September 2020; pp. 173–178, doi:10.1109/MFI49285.2020.9235248.
Academic Editors: Florian Pfaff, Sukhan Lee and Uwe D. Hanebeck
Sensors 2021, 21(15), 5004; https://doi.org/10.3390/s21155004
Received: 18 May 2021 / Revised: 14 July 2021 / Accepted: 15 July 2021 / Published: 23 July 2021
(This article belongs to the Special Issue Multisensor Fusion and Integration)
The fusion of motion data is key in the fields of robotic and automated driving. Most existing approaches are filter-based or pose-graph-based. By using filter-based approaches, parameters should be set very carefully and the motion data can usually only be fused in a time forward direction. Pose-graph-based approaches can fuse data in time forward and backward directions. However, pre-integration is needed by applying measurements from inertial measurement units. Additionally, both approaches only provide discrete fusion results. In this work, we address this problem and present a uniform B-spline-based continuous fusion approach, which can fuse motion measurements from an inertial measurement unit and pose data from other localization systems robustly, accurately and efficiently. In our continuous fusion approach, an axis-angle is applied as our rotation representation method and uniform B-spline as the back-end optimization base. Evaluation results performed on the real world data show that our approach provides accurate, robust and continuous fusion results, which again supports our continuous fusion concept. View Full-Text
Keywords: uniform B-spline; data fusion; ego-motion estimation; axis-angle; Rodrigues’ formula; inertial measurement units (IMU); Simultaneous Localization and Mapping (SLAM) uniform B-spline; data fusion; ego-motion estimation; axis-angle; Rodrigues’ formula; inertial measurement units (IMU); Simultaneous Localization and Mapping (SLAM)
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MDPI and ACS Style

Hu, H.; Beck, J.; Lauer, M.; Stiller, C. Continuous Fusion of Motion Data Using an Axis-Angle Rotation Representation with Uniform B-Spline. Sensors 2021, 21, 5004. https://doi.org/10.3390/s21155004

AMA Style

Hu H, Beck J, Lauer M, Stiller C. Continuous Fusion of Motion Data Using an Axis-Angle Rotation Representation with Uniform B-Spline. Sensors. 2021; 21(15):5004. https://doi.org/10.3390/s21155004

Chicago/Turabian Style

Hu, Haohao, Johannes Beck, Martin Lauer, and Christoph Stiller. 2021. "Continuous Fusion of Motion Data Using an Axis-Angle Rotation Representation with Uniform B-Spline" Sensors 21, no. 15: 5004. https://doi.org/10.3390/s21155004

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