分层式三维室内地图分类方法及更新机制  

Classification method and updating mechanism of hierarchical 3D indoor map

作　　者：冯光升 张晓雪 王慧强 李冰洋[1] 袁泉[2] 陈诗军[2] 陈大伟[2] FENG Guangsheng;ZHANG Xiaoxue;WANG Huiqiang;LI Bingyang;YUAN Quan;CHEN Shijun;CHEN Dawei(College of Computer Science and Technology,Harbin Engineering University,Harbin Heilongjiang 150001,China;Zhongxing Telecommunication Equipment Corporation,Shenzhen Guangdong 518055,China)

机构地区：[1]哈尔滨工程大学计算机与科学技术学院,哈尔滨150001 [2]中兴通讯股份有限公司,广东深圳518055

出　　处：《计算机应用》2019年第1期78-81,共4页journal of Computer Applications

基　　金：国家科技重大专项(2016ZX03001023-005);中央高校基本科研业务费专项(HEUCF100601);中兴产学研合作项目(2016ZTE01-03-06);中兴通讯产学研合作论坛项目(2018ZTE)~~

摘　　要：针对现有的地图更新方法,在室内地图环境下的效果并不理想的问题,提出了一种分层式的室内地图更新方法。首先以室内物体的活动性为参数,然后进行层次的划分来减少更新数据的数量,最后利用卷积神经网络(CNN)对室内数据进行归属层次的判定。实验结果表明,与版本式更新方法相比,所提算法的更新时间降低了27个百分点;与增量式更新方法相比,其更新时间在更新项大于100后逐渐降低。与增量式更新方法相比更新包大小降低了6. 2个百分点,且在数据项小于200之前其更新包一直小于版本式更新方法。所提方法可以显著提高室内地图的更新效率。For the fact that existing map updating methods are not good at map updating in indoor map environments,a hierarchical indoor map updating method was proposed.Firstly,the activity of indoor objects was taken as a parameter.Then,the division of hierarchy was performed to reduce the amount of updated data.Finally,a Convolutional Neural Network(CNN)was used to determine the attribution level of indoor data in network.The experimental results show that compared with the version update method,the update time of the proposed method is reduced by 27 percentage points,and the update time is gradually reduced compared with the incremental update method after the update item number is greater than 100.Compared with the incremental update method,the update package size of the proposed method is reduced by 6.2 percentage points,and its update package is always smaller than that of the version update method before the data item number is less than 200.Therefore,the proposed method can significantly improve the updating efficiency of indoor maps.

关 键 词：室内地图 地图更新方法 分层式更新 卷积神经网络 增量式更新 版本式更新 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]