动态环境下的语义地图构建  

Construction of Semantic Mapping in Dynamic Environments

作　　者：齐少华 徐和根[1] 万友文 付豪 QI Shao-hua;XU He-gen;WAN You-wen;FU Hao(College of Electronics and Information Engineering,Tongji University,Shanghai 201804,China)

机构地区：[1]同济大学电子与信息工程学院,上海201804

出　　处：《计算机科学》2020年第9期198-203,共6页Computer Science

摘　　要：三维语义地图在移动机器人的导航、路径规划、智能抓取、人机交互等任务中有着关键的作用,因此如何实时地构建三维语义地图尤为重要。当前同时定位和地图构建(Simultaneous Localization And Mapping,SLAM)算法已经可以达到较高的定位和制图精度,但是在动态环境下如何通过剔除动态物体来获得较高的定位精度,以及理解周围场景中存在的物体及其位置信息等问题没有得到很好的解决。在此,文中提出了一种可在动态环境下构建语义地图的算法。该算法在ORB-SLAM2上进行改进,在跟踪线程中加入动静点检测算法来剔除检测为动点的特征点,提高了动态环境下的定位精度;添加目标检测线程对关键图像进行目标检测,在地图构建线程中构建Octo-Map地图,同时根据检测结果构建3D目标数据库。为了证明该算法的可行性,以实验室为测试环境,分别进行了目标检测、动态点检测、三维目标信息获取和动态环境下语义地图构建的实验。在目标检测实验中,训练了速度和精度较高的目标检测网络——mobilenet-v2-ssdlite,检测速度可以达到7帧/秒,基本可以实现实时检测。在动态点检测中,采用光流法剔除动态点,处理速度为16.5帧/秒。文中创建了数据集来评测算法性能,相比原版ORB-SLAM2算法,结合光流法后的算法的定位精度提高了5倍;在三维目标信息获取上,采用了基于深度滤波和基于点云分割两种方法,结果表明后者的3D目标获取更为精确。最后,对整个实验室进行动态环境下的语义地图构建,构建Octo-Map稠密地图,根据检测结果构建3D目标数据库,并将目标尺寸和位置的检测值与真实值进行对比,误差均在5厘米以内。实验结果表明所提算法具有较高的精度和实时性。Three-dimensional semantic maps play a key role in tasks such as robot navigation,path planning,intelligent grasping and human-computer interaction.So how to construct 3D semantic maps in real time is especially important.The current SLAM(simultaneous localization and mapping)algorithm can achieve higher positioning and mapping accuracy.However,how to eliminate dynamic objects to obtain higher positioning accuracy in a dynamic environment,and to understand the existence of objects and their location information in the surrounding scenes are still not well solved.This paper presents an algorithm for constructing semantic maps in a dynamic environment.This algorithm is improved on ORB-SLAM2.The dynamic and static point detection algorithm is added to the tracking thread to eliminate the feature points detected as dynamic feature points,which improves the positioning accuracy in dynamic environment.Object detection threads are added to detect key images.The mapping threads are added with the Octo-Map dense map construction.At the same time,the 3D object database is constructed according to the detection results.In order to prove the feasibility of the algorithm,the laboratory is used as the test environment,and the object detection,dynamic point detection,3D target information acquisition,and semantic map construction experiments in the dynamic environment are performed.In the object detection experiment,a high-speed and high-precision object detection network,mobilenet-v2-ssdlite,is trained,which can reach a detection speed of 7 frames/s,which can basically achieve real-time detection.In dynamic point detection,the optical flow method was used to eliminate dynamic point,processing speed is 16.5 frames/s.And this paper creates a data set to evaluate the performance of the algorithm.Compared with the original ORB-SLAM2 algorithm,the positioning accuracy is improved by 5 times after combining with the optical flow method.For the acquisition of three-dimensional object information,two methods based on depth filtering a

关 键 词：语义地图构建 动态点检测 目标检测 视觉SLAM 

分 类 号：TP249[自动化与计算机技术—检测技术与自动化装置]