为了识别植被场景中的叶片和相邻障碍物,提出了一种三维激光雷达的目标检测算法。以雷达点云中的相邻点构建邻域特征,提取新的特征参数作为判别依据,采用期望最大算法求得混合高斯模型以表征特征参数的分布情况;最后,利用马尔可夫随机场建立先验模型,在最大后验概率框架下采用图割法进行求解,得到最优目标函数。该算法已成功应用于无人驾驶平台。研究结果表明,该算法能有效地识别叶片及其邻接障碍物,可以清楚地分辨障碍物边界。与传统算法相比,该算法具有更高的稳健性和准确率,且其实时性满足实际应用的需求。

In order to identify foliage and the adjacent obstacles in the vegetation scenes, an object detection algorithm of three-dimensional laser radar is proposed. The neighborhood characteristics of neighboring points are constructed in point cloud, and new characteristic parameters are extracted as determining criterion. Then the Gaussian mixture model is obtained by using the maximum expectation algorithm to characterize the distribution of the parameters. Finally, the priori model is established by using Markov random field. The optimal objective function is obtained by the graph-cut method under the maximum posteriori probability framework. This algorithm has been successfully applied to the unmanned platform. The experimental results show that the algorithm can effectively identify foliage and their adjacent obstacles, and the boundaries of obstacles can be detected clearly. Compared with traditional algorithms, the proposed algorithm is more robust and accurate, and its response time meets the demand of practical applications.