中文摘要：

提出一种激光点云数据关联决策算法。基于判别图模型，提取并智能管理激光点云的多重形状特征，通过最大伪似然学习优化局部特征和配对特征的权重；应用最大和概率推理实现对图模型隐节点状态的估计，进而将激光点关联映射为最大后验概率的配置回溯问题；实验结果验证了所提出算法比传统算法具有更好的性能。

英文摘要：

A laser point clouds data association decision algorithm is proposed. The multi-geometric feature extraction and intelligent management for the laser point clouds are investigated with the discriminative graphical model. The maximum pseudo-likelihood learning is employed to optimize the weights of the local and pairwise features. And the states of the hidden nodes in the graph are estimated with max-sum probabilistic inference. Furthermore, the laser point association is tackled as the maximum a posteriori （MAP） configuration backtracking problem. The experiment results demonstrate that the proposed algorithm outperforms traditional algorithms.