Continuous Fusion of IMU and Pose Data using Uniform B-Spline

In this work, we present an uniform B-spline based continuous fusion approach, which fuses the motion data from an inertial measurement unit and the pose data from a visual localization system accurately, efficiently and continu-ously. Currently, in the domain of robotics and autonomous driving, most of the ego motion fusion approaches are filter based or pose graph based. By using the filter based approaches like the Kalman Filter or the Particle Filter, usually, many parameters should be set carefully, which is a big overhead. Besides that, the filter based approaches can only fuse data in a time forwards direction, which is a big disadvantage in processing async data. Since the pose graph based approaches only fuse the pose data, the inertial measurement unit data should be integrated to estimate the corresponding pose data firstly, which can however bring accumulated error into the fusion system. Additionally, the filter based approaches and the pose graph based approaches only provide discrete fusion results, which may decrease the accuracy of the data processing steps afterwards. Since the fusion approach is generally needed for robots and automated driving vehicles, it is a major goal to make it more accurate, robust, efficient and continuous. Therefore, in this work, we address this problem and apply the axis-angle rotation representation method, the Rodrigues’ formula and the uniform B-spline implementation to solve the ego motion fusion problem continuously. Evaluation results performed on the real world data show that our approach provides accurate, robust and continuous fusion results.