机器人远程点云分割框架设计与实现Design and Implementation of Robot Remote Point Cloud Segmentation Framework
赵锦杰;蒋佳芹;姚剑;
摘要(Abstract):
针对机器人端运算能力有限、难以快速进行3D点云分割的问题,提出了一种基于远程过程调用(remote procedure call,RPC)的点云远程分割框架,实现了机器人端快速获取点云分割结果的功能。首先位于机器人端采集点云数据;然后把点云序列化为Protocol Buffers的格式;服务端对点云采样、分割并把结果返回至机器人端;最后机器人端获取服务端结果并反序列化得到点云分割结果。实验表明,该方法和常用的RESTful接口相比,在保障实时性的基础上,能大幅度减少流量消耗,从而提升了机器人点云分割的作业效率。
关键词(KeyWords): 机器人;点云分割;远程过程调用;序列化;实时性
基金项目(Foundation): 国家重点研发计划(2017YFB1302400)
作者(Authors): 赵锦杰;蒋佳芹;姚剑;
DOI: 10.14188/j.2095-6045.2020073
参考文献(References):
- [1]易柯敏,沈艳霞.激光SLAM导航移动机器人定位算法研究综述[J].机器人技术与应用,2019(5):25-28
- [2] LeCun Y,Bengio Y,Hinton G. Deep Learning[J].Nature,2015,521(7 553):436-444
- [3] Nguyen A,Le B. 3D Point Cloud Segmentation:A Survey[C]. 2013 6th IEEE conference on Robotics,Automation and Mechatronics(RAM),IEEE, Manila,Philippines,2013
- [4]高芳,赵志耘,张旭,等.全球5G发展现状概览[J].全球科技经济瞭望,2014,29(7):59-67
- [5]张颖,刘亚文,苗堃.基于空间上下文关联的车载点云聚类方法[J].测绘地理信息,2019,44(4):116-121
- [6]黄文坚,唐源. TensorFlow实战[M].北京:电子工业出版社,2017
- [7]施瓦茨.高性能MySQL[M].王小东,李军,康建勋,译.北京:电子工业出版社,2010
- [8] Qi C R,Yi L,Su H,et al. PointNet++:Deep Hierarchical Feature Learning on Point Sets in a Metric Space[J]. Advances in Neural Information Processing Systems,2017:5 099-5 108
- [9]隋心怡,王瑞刚,梁小江.基于Google Protocol Buffer的即时通讯系统设计[J].电子科技. 2017,(1):119-122
- [10] Gourley D,Totty B. HTTP权威指南[M].陈涓,赵振平,译.北京:人民邮电出版社,2012
- [11] Fielding R T,Taylor R N. Architectural Styles and the Design of Network-Based Software Architectures[D]. Irvine:University of California,2000
- [12] Nedelcu C. Nginx HTTP Server:Adopt Nginx for Your Web Applications to Make the Most of Your Infrastructure and Serve Pages Faster than Ever[M]. Birmingham,UK:Packt Publishing,2010