顾及扫描上下文的预测与判决相结合的点云在线分类方法

项目名称： 顾及扫描上下文的预测与判决相结合的点云在线分类方法

项目编号： No.41501499

项目类型： 青年科学基金项目

立项/批准年度： 2016

项目学科： 天文学、地球科学

项目作者： 郭波

作者单位： 广东工业大学

项目金额： 20万元

中文摘要： 三维激光扫描技术目前广泛应用于场景理解及地物类别区分，在线点云分类能够为无人驾驶及导航提供行进路线上场景语义信息，用于路线规划及避障。传统点云分类方法，由于特征使用量多，在分类过程中未依据场景变化在线更新分类结构，不能同时满足在线分类效率及精度要求。本课题结合扫描上下文地物联系，采用先预测后判决的思想，研究随场景中地物类别变化，在线更新的分类优化方法及模型。为此需要研究：构建基于空间相关性的类别传播模型，依据扫描顺序中已分类地物，对行进路线上的待分类地物的类别概率进行预测；构建类别非均衡的决策树分类模型，按照待分类对象预测类别的概率，依次从高到低对可能的类别进行序列判决，减少分类过程中非相关类别特征使用量。最后，搭建实验平台，对模型和算法进行综合试验验证。本课题能够解决兼顾分类精度及效率的点云在线分类问题，为场景感知及无人导航提供技术支撑，提升基于点云场景理解的理论水平。

中文关键词： 点云分类；在线；上下文；预测；类别非均衡判决

英文摘要： 3D laser scanning technique is being widely used for scene understanding and object classification. Online classification of point cloud data could be used for route planning and barrier avoiding of unmanned platforms based on semantic information it provided. Due to abundant features used and fixed structures of classifiers which are not changed based on scene changing, the efficiency and precision of classification could not be both satisfied by using traditional classification methods of point cloud data. Based on scanning context, this project investigates an online updating classifier which combining prediction and decision along with the objects' classes changing in scene. First, a class transmission model is designed based on spatial relationship of objects. The classes of objects on route are predicted using the classified objects which have been scanned previously. Second, a non-balanced decision tree is constructed for efficient classification according to the classes probability of prediction. This could be improve the classification efficiency due to reducing the amount of irrelevant features. Finally, in order to demonstrate the applicability of proposed models and algorithms, a prototype online classifciation system is developed and many tests are designed for analyzing the classification precision and efficiency. The development of fine modelling will provides a new method for high-precision classification efficiently. It is expected that methods developed in this project not only provide technique for scene understanding and navigation of unmanned platforms, but also promote the theory level of classification of point cloud data.

英文关键词： point cloud classification ;online ;context;prediction;non-balance decision of class