Optimization based IMU camera calibration

Optimization based IMU camera calibration,10.1109/IROS.2011.6048797,Michael Fleps,Elmar Mair,Oliver Ruepp,Michael Suppa,Darius Burschka

Optimization based IMU camera calibration  
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Inertia-visual sensor fusion has become popular due to the complementary characteristics of cameras and IMUs. Once the spatial and temporal alignment between the sensors is known, the fusion of measurements of these devices is straightforward. Determining the alignment, however, is a chal- lenging problem. Especially the spatial translation estimation has turned out to be difficult, mainly due to limitations of camera dynamics and noisy accelerometer measurements. Up to now, filtering-based approaches for this calibration problem are largely prevalent. However, we are not convinced that calibration, as an offline step, is necessarily a filtering issue, and we explore the benefits of interpreting it as a batch-optimization problem. To this end, we show how to model the IMU-camera calibration problem in a nonlinear optimization framework by modeling the sensors' trajectory, and we present experiments comparing this approach to filtering and system identification techniques. The results are based both on simulated and real data, showing that our approach compares favorably to conventional methods. I. INTRODUCTION Proper localization is a crucial issue in robotic applica- tions. However, applications based on localization become more and more demanding regarding dynamics. Mobile robots move quicker and many applications are ported on hand-held devices. As a consequence, the need for sensors which are able to deal with such high dynamics increases permanently. The best choice to measure quick motions are inertial measurement units (IMUs), consisting of a gyroscope and an accelerometer for each of the three spatial axes. While high quality IMUs are common, e.g., in nautics and aeronautics applications, they are usually considered too expensive for robotic applications. The development of cheap gyros and accelerometers based on microelectromechanical systems (MEMS) reduced the cost of IMUs drastically and helped to introduce them to many new application areas. The drawback of these MEMS sensors is that they are quite prone to noise, and large rotations and accelerations are needed to produce measurements exhibiting a useful signal to noise ratio.
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