首页|Recent Studies from Xidian University Add New Data to Robotics and Automation (E pl-vins: Efficient Point-line Fusion Visual-inertial Slam With Lk-rg Line Tracki ng Method and 2-dof Line Optimization)
Recent Studies from Xidian University Add New Data to Robotics and Automation (E pl-vins: Efficient Point-line Fusion Visual-inertial Slam With Lk-rg Line Tracki ng Method and 2-dof Line Optimization)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Research findings on Robotics - Robotics and Auto mation are discussed in a new report. According to news originating from Guangzh ou, People’s Republic of China, by NewsRx correspondents, research stated, “The performance of a visual SLAM system based on point features significantly dimini shes in low-textured environments due to the challenges in extracting sufficient and reliable points. The fusion of line and point features improves SLAM system performance by providing additional visual constraints.” Financial support for this research came from Guangzhou Key Research and Develop ment Program. Our news journalists obtained a quote from the research from Xidian University, “To improve the efficiency and accuracy of the point-line-based SLAM system, thi s letter introduces EPL-VINS, an efficient point-line fusion visual-inertial SLA M system. We present the LK-RG line segment tracking method, which combines the Lucas-Kanade (LK) algorithm with the Region Growing (RG) algorithm from the Line Segment Detector (LSD). Moreover, we introduce a novel representation for spati al lines, based on which we construct line reprojection residuals and conduct a 2-degrees-of-freedom (2-DoF) optimization of spatial lines in the back-end. The proposed system is built upon VINS-Fusion, and supports the original three senso r suites: a monocular with an IMU, stereo cameras, and stereo cameras with an IM U. The experimental results show that the LK-RG method exhibits rapid processing and a high success rate in line segments matching.”
GuangzhouPeople’s Republic of ChinaA siaRobotics and AutomationRoboticsAlgorithmsXidian University
NETL
NSTL
