Recently, multi-sensors fusion has achieved significant progress in the field of automobility to improve navigation and position performance. As the prerequisite of the fusion algorithm, the demand for the extrinsic calibration of multi-sensors is growing. To calculate the extrinsic parameter, many researches have been dedicated to the two-step method, which integrates the respective calibration in pairs. It is inefficient and incompact because of losing sight of the constrain of all sensors. With regard to remove this burden, an optimization-based IMU/Lidar/Camera co-calibration method is proposed in the paper. Firstly, the IMU/camera and IMU/lidar online calibrations are conducted, respectively. Then, the corner and surface feature points in the chessboard are associated with the coarse result and the camera/lidar constraint is constructed. Finally, construct the co-calibration optimization to refine all extrinsic parameters. We evaluate the performance of the proposed scheme in simulation and the result demonstrates that our proposed method outperforms the two-step method.
翻译：最近,多传感器融合在自动化领域取得了显著进展,以改善导航和定位性能。作为聚合算法的先决条件,对多传感器外部校准的需求正在增长。为了计算外部参数,许多研究都专门针对两步方法,该方法将各自的校准结合成对,由于忽视了所有传感器的制约,该方法效率低且不相干。关于消除这一负担,在文件中提出了基于优化的IMU/Lidar/Camera共校校校方法。首先,IMU/amera和IMU/lidar在线校准分别进行。然后,国际金属监测网的角点和地貌点与粗糙的结果相联系,并构建了摄像/激光限制。最后,构建共同校准优化以完善所有外观参数。我们评估了模拟中拟议办法的性能,结果显示我们提议的方法超越了两步方法。
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