【摘 要】
：
为了解决基于无人直升机的机载激光雷达(Lidar)系统中获得三维激光点云数据的效率低、精度低问题,提出了一种可获得高精度三维点云数据的解决方案;从系统点云数据生成原理分
【机 构】
：
华南理工大学自动化科学与工程学院,西安长庆科技工程有限责任公司,
论文部分内容阅读
为了解决基于无人直升机的机载激光雷达(Lidar)系统中获得三维激光点云数据的效率低、精度低问题,提出了一种可获得高精度三维点云数据的解决方案;从系统点云数据生成原理分析影响点云精度的因素;实现十一阶扩展卡尔曼算法对多传感器数据进行数据融合处理,充分利用了不同传感器的优点;改进的扩展卡尔曼融合算法,不但有效地降低噪声和干扰对系统影响,而且提高了激光雷达系统点云数据的可靠性和精度;实验结果验证了算法的正确性和点云数据的精度。
In order to solve the problem of low efficiency and low accuracy of 3D laser point cloud data acquisition in unmanned helicopter-based Lidar system, a solution to obtain high-precision 3D point cloud data is proposed. Data generation principle analysis of the factors that affect the accuracy of point cloud; Eleventh-order extended Kalman algorithm for data fusion processing of multi-sensor data, take full advantage of the advantages of different sensors; improved extended Kalman fusion algorithm, not only effectively reduce noise and Interfere with the system, but also improve the reliability and accuracy of point cloud data in LIDAR system. Experimental results verify the accuracy of the algorithm and point cloud data.
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