【作者】 刘钊；

【导师】 戴斌；

【作者基本信息】 国防科学技术大学 ， 控制科学与工程， 2008， 硕士

【摘要】 论文以陆地自主车(Autonomous Land Vehicle, ALV)为背景,利用研究室已有的自主越野车辆平台,对多传感器信息融合技术中时空一致问题进行了深入的研究,在此基础上实现了激光雷达和立体视觉信息的融合障碍检测。多传感器时空一致是将各个不同来源的数据变换到同一个时空基准的过程,包括时间同步和空间对准两方面内容。时间同步是把各个传感器的时间统一到参考标准时间上；空间对准则要建立从各个传感器不同坐标系到一个统一的基准坐标系的变换关系(ALV传感器的空间对准必须通过各个传感器间的空间标定来完成)。本文的主要研究工作包括：(1)在基于局域网的时间同步方法的基础上,提出了一种用于多传感器系统时间同步的改进算法,该算法通过多台客户端与服务器的数据交换来实现网络中各台计算机的时间同步。仿真实验表明该算法可以在网络延时变化的情况下达到毫秒级以下的时间同步精度。(2)提出了两种传感器空间标定算法。利用单线激光雷达几何扫描模型,设计了一种激光雷达外标定算法；提出并设计了一种平面模板标定算法,完成了激光雷达和图像的直接标定。实车实验验证了两种算法的有效性。(3)研究了激光雷达和立体视觉摄像机的信息融合问题,实现了一种基于D-S证据理论的融合障碍检测算法。实车平台的静止实验和运动实验表明该融合算法可以正确的融合激光雷达和立体视觉的信息,获得可靠的环境描述。更多还原

【Abstract】 Based on Autonomous Land Vehicle (ALV), this thesis investigates the problem of multi-sensor’s time-space consistency. Fusion of lidar and stereo vision information is applied to detect obstacles.Multi-sensor’s time-space consistency is to transform different sources of information to the same time-space framework, which includes two aspects:one is the time synchronism and the other is the space alignment. Time synchronism is to unify each sensor’s time in the reference standard time; space alignment establishes the relationship of the transformation from different sensor coordinate systems to the reference coordinate system (ALV sensor’s space alignment is based on the accurate sensor calibration).The main works of the thesis are as follows:(1) An improved time synchronism algorithm is proposed based on local area network in multi-sensor system, the algorithm can synchronize each computer’s time through the data exchange between server and client. Simulation results show that our algorithm can meet the time synchronization accuracy need (<1ms) when network delay varys.(2) Two sensor calibration algorithms are proposed. One is single-line lidar exterior calibration algorithm based on the lidar geometrical model; the other is the lidar and image direct calibration using the board designed by the author. Real vehicle experiments verify the validity of both algorithms.(3) The fusion problem of the Lidar and stereo vision information is studied using the D-S evidence theory to detect obstacles. Both static and kinetic real vehicle experiments indicate that the algorithm can fuse Lidar and stereo vision information successfully and obtain reliable environment description.更多还原