This work presents a novel extrinsic calibration estimation algorithm between a 3D Lidar and an IMU using an Extended Kalman Filter which exploits the motion based calibration constraint for state update. The steps include, data collection by moving the Lidar Inertial sensor suite randomly along all degrees of freedom, determination of the inter sensor rotation by using rotational component of the aforementioned motion based calibration constraint in a least squares optimization framework, and finally determination of inter sensor translation using the motion based calibration constraint in an Extended Kalman Filter (EKF) framework. We experimentally validate our method on data collected in our lab.
翻译:这项工作展示了3D Lidar 和 IMU 之间使用扩展 Kalman 过滤器的新型外部校准估计算法,该算法利用基于运动的校准限制进行国家更新。 步骤包括:通过随机沿所有自由度移动Lidar Inertial传感器套件来收集数据;通过在最小方形优化框架内使用上述基于运动的校准限制的旋转部件确定传感器之间的旋转;以及利用基于运动的校准限制在扩展 Kalman 过滤器(EKF) 框架内最终确定传感器之间的翻译。 我们实验地验证了我们在实验室收集的数据方法 。