本文選題：同時定位與地圖構建 + 點線面特征融合　； 參考：《機器人》2017年02期

【摘要】：基于點特征的視覺SLAM(同時定位與地圖構建)算法存在計算量大、環(huán)境存儲空間負荷高、定位誤差較大的問題,為此,提出了一種基于點、線段、平面特征融合的視覺SLAM算法——PLP-SLAM.在擴展卡爾曼濾波(EKF)框架下,首先利用點特征估計機器人當前位姿,然后構建了基于點、線、平面特征的觀測模型,最后建立了帶平面約束的線段特征數(shù)據(jù)關聯(lián)方法及系統(tǒng)狀態(tài)更新模型,并利用線段和平面特征描述環(huán)境信息.在公開數(shù)據(jù)集上進行了實驗,結果表明,本文PLP-SLAM算法能夠成功完成SLAM任務,平均定位誤差為2.3 m,優(yōu)于基于點特征的SLAM方法,并通過基于不同特征的SLAM實驗表明了本文提出的點、線、面特征融合的優(yōu)越性.
[Abstract]:The visual slam (simultaneous location and map construction) algorithm based on point feature has the problems of large computation, high storage space load and large positioning error. Therefore, a new algorithm based on point and line segment is proposed. Visual SLAM algorithm for plane feature Fusion PLP-SLAM. In the framework of extended Kalman filter (EKF), the current position and attitude of the robot are estimated by using the point feature, and then the observation model based on the point, line and plane features is constructed. Finally, a line segment feature data association method with plane constraints and a system state update model are established, and line segments and plane features are used to describe the environmental information. Experiments on the open dataset show that the PLP-SLAM algorithm can successfully accomplish the SLAM task, and the average localization error is 2.3 m, which is better than the SLAM method based on the point feature. The point proposed in this paper is shown by the SLAM experiment based on different features. The superiority of line and surface feature fusion.
【作者單位】： 中國民航大學計算機科學與技術學院;福建省信息處理與智能控制重點實驗室(閩江學院);
【基金】：國家自然科學基金(61305107,U1333109) 天津市應用基礎與前沿技術研究計劃重點項目(14JCZDJC32500) 中央高校基本科研業(yè)務費(3122016B006) 福建省信息處理與智能控制重點實驗室開放課題(MJUKF201732) 福建省科技廳引導性課題(2015H0031)
【分類號】：TP242;TP391.41
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本文編號：1948460