We demonstrate a multi-lidar calibration framework for large mobile platforms that jointly calibrate the extrinsic parameters of non-overlapping Field-of-View (FoV) lidar sensors, without the need for any external calibration aid. The method starts by estimating the pose of each lidar in its corresponding sensor frame in between subsequent timestamps. Since the pose estimates from the lidars are not necessarily synchronous, we first align the poses using a Dual Quaternion (DQ) based Screw Linear Interpolation. Afterward, a Hand-Eye based calibration problem is solved using the DQ-based formulation to recover the extrinsics. Furthermore, we verify the extrinsics by matching chosen lidar semantic features, obtained by projecting the lidar data into the camera perspective after time alignment using vehicle kinematics. Experimental results on the data collected from a Scania vehicle [$\sim$ 1 Km sequence] demonstrate the ability of our approach to obtain better calibration parameters than the provided vehicle CAD model calibration parameters. This setup can also be scaled to any combination of multiple lidars.
翻译:我们展示了大型移动平台的多升校准框架,这些平台联合校准了非重叠现场(FoV)激光雷达传感器的外部参数,而不需要任何外部校准协助。该方法首先通过估计每个激光雷达在相应的传感器框中的位置,然后在随后的时标之间进行相应的传感器框。由于激光雷达的表面估计不一定同步,我们首先使用基于螺旋线性内插的双夸度(DQ)来对阵容进行校准。随后,基于手眼的校准问题通过基于DQ的配方来解决,以恢复外部校准。此外,我们通过将激光雷达数据投射到相机视野中,在使用车辆运动学校准后,通过将激光雷达数据投射到镜头的角度,对阵列进行校准。从Scrania飞行器收集的数据的实验结果[$sim1Km序列]表明,我们的方法能够获得比所提供的车辆CAD模型校准参数更好的校准参数。此外,我们还可以将这一设置扩大为多个激光的组合。