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 low efficiency and low accuracy problems of generating three-dimensional point cloud data in Airborne Light Detection and Ranging (Lidar) system, which based on unmanned helicopter, a high precision solution was proposed. Factors that affect the accuracy of the point cloud were analyzed. Implement of the eleven status extended Katman filter algorithm in multi- sensor data fusion, which full use of the advantages of different sensors. We improve the extended Kalman filter algorithm, which not only effectively reduce the impact of noise and interference but also increase the reliability and accuracy of point cloud in Lidar system. The experimental results indicate correctness of the algorithm and accuracy of the point cloud.
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