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张宁 3D Lidar-IMU Calibration Based on Upsampled Preintegrated Measurements for Motion Distortion Correction
"链接：https://pan.baidu.com/s/1GhIlgccbJx5R0CjdyO8lbw
提取码：t05m "

用于运动失真校正的基于超采样预积分测量的三维激光雷达IMU校准

Abstract— In this paper, we present a probabilistic framework to recover the extrinsic calibration parameters of a lidar-IMU sensing system. Unlike global-shutter cameras, lidars do not take single snapshots of the environment. Instead, lidars collect a succession of 3D-points generally grouped in scans.If these points are assumed to be expressed in a common frame, this becomes an issue when the sensor moves rapidly in the environment causing motion distortion. The fundamental idea of our proposed framework is to use preintegration over interpolated inertial measurements to characterise the motion distortion in each lidar scan. Moreover, by using a set of planes as a calibration target, the proposed method makes use of lidar point-to-plane distances to jointly calibrate and localise the system using on-manifold optimisation. The calibration does not rely on a predefined target as arbitrary planes are detected and modelled in the first lidar scan. Simulated and real data are used to show the effectiveness of the proposed method.

在本文中，我们提出了一个概率框架来恢复激光雷达-IMU传感系统的外部校准参数。与全局快门相机不同，激光雷达不会拍摄环境的单个快照。 相反，激光雷达会收集一系列通常在扫描中分组的3D点。如果假设这些点在公共帧中表示，则当传感器在环境中快速移动导致运动失真时，这成为问题。我们提出的框架的基本思想是使用预积分而不是内插惯性测量来表征每个激光雷达扫描中的运动失真。此外，通过使用一组平面作为校准目标，所提出的方法利用激光雷达点到平面距离来使用流形上优化来联合校准和定位系统。校准不依赖于预定目标，因为在第一激光雷达扫描中检测并建模任意平面。 模拟和实际数据用于显示所提出方法的有效性。