Abstract
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.
Highlights
In the past years, robotic, micro aerial and automated driving technologies have become more and more popular and are intensively investigated
Both approaches only provide discrete fusion results. 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
Filter-based or pose-graph-based approaches have problems by fusing asynchronous motion measurements with different types and frequencies. We address these problems and present a uniform B-spline-based continuous motion fusion approach, which is shown in Figure 1 and applies asynchronous motion measurements with different types and frequencies and can be optimized in both time forward and backward directions simultaneously
Summary
Robotic, micro aerial and automated driving technologies have become more and more popular and are intensively investigated. These different sensor systems provide different types of measurements with different frequencies: the GNSS provides global position measurements; feature-based localization systems provide pose measurements; the odometer system provides pose difference measurements and IMU sensors provide linear acceleration and angular velocity measurements with a very high frequency Even though this topic is well studied and many approaches have been developed in the past, it is still challenging to fuse the measurements of different types and frequencies accurately and efficiently with as little as possible loss of information. We address these problems and present a uniform B-spline-based continuous motion fusion approach, which is shown in Figure 1 and applies asynchronous motion measurements with different types and frequencies and can be optimized in both time forward and backward directions simultaneously. Is processed in the time forward and backward directions to refine fusion results; provides pose, velocity and acceleration fusion results continuously in time
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