高鲁棒性两阶段激光雷达惯性测量单元外参在线标定算法

针对车辆在近似平面上运动时，退化的传感器数据难以标定外参的问题，提出了一种高鲁棒性两阶段激光雷达惯性测量单元(LiDAR-IMU)外参在线标定算法。标定算法包括剔除外点的解析解初值计算和非线性滑窗在线迭代优化两个阶段。第一阶段剔除预数据集中的外点，以滑动窗口的形式多次求解只包含旋转分量的手眼标定模型，并改进解析解筛选的条件，求解出多解加权旋转外参的SVD解析解。第二阶段最小化包含外参的残差函数，以旋转解析解为初值滑动窗口迭代优化六自由度外参，使外参快速收敛，并在退化运动和错误历史约束过大时固定外参，避免外参退化。与原始算法对比，该算法对退化的传感器数据具有鲁棒性，可在无外参初值的情况下实现精准鲁棒地在线标定外参。

Highly Robust Two-stage LiDAR-IMU External Parameter Online Calibration Algorithm

Aiming at the problems that the degraded sensor data were difficult to calibrate external parameters when the vehicles were moving in an approximate plane, a highly robust two-stage LiDAR-IMU external parameters online calibration algorithm was proposed. The calibration algorithm included two stages: initial value calculation and online iterative optimization using nonlinear sliding window. In the first stage, the outliers in the pre-data set were eliminated, the hand-eye calibration model that contained only the rotation components was solved multiple times in the form of a sliding window, meanwhile, the conditions of the analytical solution screening were improved, the singular value decomposition（SVD） analytical solution of multiple weighted rotation external parameters was solved. The second stage minimized the residual function containing the external parameters, and used the rotational analytical solution as the initial value sliding window to iteratively optimize the six-degree-of-freedom external parameters, so that the external parameters converged quickly. In order to avoid the degradation of external parameters, when the degenerate motion and error history constraints were too large, the external parameters would be fixed. Compared with the original algorithm, the algorithm is robust to degraded sensor data, which may achieve accurate and robust online calibration of external parameters without the initial values of the external parameters.