基于判别图模型的激光点云数据关联决策  

Laser point clouds data association decision based on discriminative graphical model

作　　者：孙作雷[1] 张波[2] 曾连荪[1] 朱大奇[1] 

机构地区：[1]上海海事大学信息工程学院,上海201306 [2]中国科学院上海高等研究院,上海201210

出　　处：《控制与决策》2014年第5期885-888,共4页Control and Decision

基　　金：国家自然科学基金项目(61105097;51279098);上海市教育委员会科研创新项目(13YZ081)

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.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.

关 键 词：激光点云 判别图模型 数据关联 概率推理 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]