文献类型：期刊文献

英文题名：Research on Moving Target Tracking Algorithm Based on Lidar and Visual Fusion

作者：Guo, Xiaoxiao[1];Liu, Yuansheng[2,3,4];Zhong, Qixue[2,5];Chai, Mengna[2,5]

第一作者：Guo, Xiaoxiao

通讯作者：Liu, YS[1]

机构：[1]Beijing Union Univ, Beijing Key Lab Informat Serv Engn, 97 Beisihuan East Rd, Beijing 100101, Peoples R China;[2]Beijing Union Univ, Beijing Engn Res Ctr Smart Mech Innovat Design Se, 97 Beisihuan East Rd, Beijing 100101, Peoples R China;[3]Beijing Union Univ, Coll Robot, 97 Beisihuan East Rd, Beijing 100101, Peoples R China;[4]Beijing Union Univ, Wheeled Mobile Robot Dept, 97 Beisihuan East Rd, Beijing 100101, Peoples R China;[5]Beijing Engn Res Ctr Smart Mech Innovat Design Se, 97 Beisihuan East Rd, Beijing 100101, Peoples R China

第一机构：北京联合大学北京市信息服务工程重点实验室

通讯机构：[1]corresponding author), Beijing Union Univ, Beijing Engn Res Ctr Smart Mech Innovat Design Se, 97 Beisihuan East Rd, Beijing 100101, Peoples R China.|[11417]北京联合大学;

年份：2018

卷号：22

期号：5

起止页码：593-601

外文期刊名：JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS

收录：EI(收录号：20183905866678);Scopus(收录号：2-s2.0-85053804530);WOS:【ESCI(收录号:WOS:000446003800002)】；

基金：This work was financially supported by Academic Human Resources Development in Beijing Union University (Grant number: BPHR2017EZ02), Talents Cultivation and Cooperation Oriented to Intelligent Vehicle Industrialization (Grant number: UK-CIAPP\324), Newton Fund Project supported by Royal Academy of Engineering of UK, Beijing Municipal Education Commission (Grant number: SQKM201411417006), Supporting Plan for Cultivating High Level Teachers in Colleges and Universities in Beijing (Grant number: IDHT20170511), and Graduate Funded Project of Beijing Union University.

语种：英文

外文关键词：autonomous vehicles; target tracking; multi-sensor fusion; data association

摘要：Multi-sensor fusion and target tracking are two key technologies for the environmental awareness system of autonomous vehicles. In this paper, a moving target tracking method based on the fusion of Lidar and binocular camera is proposed. Firstly, the position information obtained by the two types of sensors is fused at decision level by using adaptive weighting algorithm, and then the Joint Probability Data Association (JPDA) algorithm is correlated with the result of fusion to achieve multi-target tracking. Tested at a curve in the campus and compared with the Extended Kalman Filter (EKF) algorithm, the experimental results show that this algorithm can effectively overcome the limitation of a single sensor and track more accurately.