基于激光雷达的运动补偿方法  

Motion Compensation Method Based on Lidar

作　　者：庞正雅 周志峰 王立端 叶珏磊 Pang Zhengya;Zhou Zhifeng;Wang Liduan;Ye Juelei(College of Mechanical and Automotive Engineering,Shanghai University of Engineering Science,Shanghai 201620,China;Shanghai Compass Satellite Navigation Technology Co.Ltd.,Shanghai 201801,China)

机构地区：[1]上海工程技术大学机械与汽车工程学院,上海201620 [2]上海司南卫星导航技术股份有限公司,上海201801

出　　处：《激光与光电子学进展》2020年第2期221-228,共8页Laser & Optoelectronics Progress

基　　金：上海市科学技术委员会科研基金(17511106700)。

摘　　要：激光雷达运动补偿是智能车动态背景目标检测中不可避免的过程。提出了一种基于激光雷达的运动补偿算法。首先通过四元数法求解车体在上一个扫描周期与当前扫描周期的位姿变化矩阵。其次,根据静态场景的特点及历史激光雷达数据帧生成的数据包,利用高斯混合模型对时间坐标系下的背景进行建模。考虑到高斯混合模型在动态场景下容易失效,通过运动补偿将动态背景转换为静态背景,再用高斯混合模型对时间列表中所有历史帧进行处理,得到T时刻运动目标的原点特征点,将特征点与当前帧中符合的点相匹配进一步细化点在当前帧中的新位置。经过实验验证,该方法成功地对背景运动进行了估计和补偿,适合应用于三维环境下实时运动目标的检测。Lidar motion compensation is a crucial step in detection of dynamic background targets in smart cars.Herein,a motion compensation algorithm is proposed based on lidar.Firstly,the pose change matrix of the vehicle body between the previous and the current scan periods is solved using quaternion method.Secondly,according to the characteristics of the static scene and the data packets generated by the historical lidar data frame,the Gaussian mixture model is used to model the background over time coordinate system.The Gaussian mixture model is prone to failure in dynamic scenes,therefore,in this study,the dynamic background is first converted into a static background by motion compensation.Then,the Gaussian mixture model is used to deal with all historical frames in the time list,and feature points of the original point of the moving target are found at T moment.The alignment of feature points with the points in the current frame is used to further refine the new position of matching points in the current frame.Experimental results show that the proposed method can successfully estimate and compensate for the background motion and is suitable for real-time detection of moving targets in 3D environments.

关 键 词：成像系统 四元数法 高斯混合模型 运动补偿 目标检测 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]