论文标题
基于连续的时间批量估计的LIDAR-IMU系统的无目标校准
Targetless Calibration of LiDAR-IMU System Based on Continuous-time Batch Estimation
论文作者
论文摘要
传感器校准是多传感器融合系统的基本块。本文提出了一种准确且可重复的LIDAR-IMU校准方法（称为Li-Calib），以校准3D激光雷达和惯性测量单元（IMU）之间的6DOF外部转换。 ％ 关于LIDAR和IMU传感器的高数据捕获率，Li-Calib采用了基于B型频道的连续时间轨迹公式，该公式比基于离散时间的方法更适合融合高率或异步测量。 ％ 此外，Li-Calib将空间分解为细胞，并识别数据关联的平面段，这使得在通常的情况下没有任何人工目标的情况下，校准问题很好地约束。我们在模拟和现实世界实验上验证了所提出的校准方法。结果表明，在常见的人为方案中，提出的方法的高精度和良好的可重复性。为了使研究社区受益，我们通过\ url {https://github.com/april-zju/lidar_imu_calib}开放代码。
Sensor calibration is the fundamental block for a multi-sensor fusion system. This paper presents an accurate and repeatable LiDAR-IMU calibration method (termed LI-Calib), to calibrate the 6-DOF extrinsic transformation between the 3D LiDAR and the Inertial Measurement Unit (IMU). % Regarding the high data capture rate for LiDAR and IMU sensors, LI-Calib adopts a continuous-time trajectory formulation based on B-Spline, which is more suitable for fusing high-rate or asynchronous measurements than discrete-time based approaches. % Additionally, LI-Calib decomposes the space into cells and identifies the planar segments for data association, which renders the calibration problem well-constrained in usual scenarios without any artificial targets. We validate the proposed calibration approach on both simulated and real-world experiments. The results demonstrate the high accuracy and good repeatability of the proposed method in common human-made scenarios. To benefit the research community, we open-source our code at \url{https://github.com/APRIL-ZJU/lidar_IMU_calib}
