基于激光结构光的视觉传感器广泛应用于焊接领域的坡口检测和焊缝跟踪。该文提出了一种基于组合激光结构光的新型视觉传感器,独特的光路结构设计避免了传感器应用于不同焊接位姿时繁琐的外参数标定,仅依靠传感器内部固有参数(应用前需校...基于激光结构光的视觉传感器广泛应用于焊接领域的坡口检测和焊缝跟踪。该文提出了一种基于组合激光结构光的新型视觉传感器,独特的光路结构设计避免了传感器应用于不同焊接位姿时繁琐的外参数标定,仅依靠传感器内部固有参数(应用前需校准)和焊接坡口图像的特征点坐标值,即可实现焊接坡口参数的在线检测,有效增强了传感器的适应性。通过对不同图像处理算法的改进和合理组合,对图像处理流程进行了优化。动态感兴趣区(region of interest,ROI)区域提取算法可快速寻获有价值的激光线和特征点所在区域,有效提升了后续图像处理速度;顶帽变换与自适应二值化组合,在将激光线灰度值均匀化的同时,实现了激光线与背景图像的有效区分。运用基于LOG(Laplacian of Gaussian)算子的边缘识别算法,可提取出激光线的单像素边缘;采用最小二乘法对所求得的激光中心线离散点进行直线拟合,通过联立直线方程求交点的方式,实现了对焊接坡口特征点图像坐标值的准确识别。借助Visual Studio平台,运用改进的图像处理算法、优化的图像处理流程和检测算法,对特征参数不同的V形焊接坡口进行检测试验,检测误差均在±4%以内,验证了所提出视觉传感器及其检测算法和图像处理流程的合理性和适用性。展开更多
Because of its high-precision,low-cost and easy-operation,Precise Point Positioning(PPP)becomes a potential and attractive positioning technique that can be applied to self-driving cars and drones.However,the reliabil...Because of its high-precision,low-cost and easy-operation,Precise Point Positioning(PPP)becomes a potential and attractive positioning technique that can be applied to self-driving cars and drones.However,the reliability and availability of PPP will be significantly degraded in the extremely difficult conditions where Global Navigation Satellite System(GNSS)signals are blocked frequently.Inertial Navigation System(INS)has been integrated with GNSS to ameliorate such situations in the last decades.Recently,the Visual-Inertial Navigation Systems(VINS)with favorable complementary characteristics is demonstrated to realize a more stable and accurate local position estimation than the INS-only.Nevertheless,the system still must rely on the global positions to eliminate the accumulated errors.In this contribution,we present a semi-tight coupling framework of multi-GNSS PPP and Stereo VINS(S-VINS),which achieves the bidirectional location transfer and sharing in two separate navigation systems.In our approach,the local positions,produced by S-VINS are integrated with multi-GNSS PPP through a graph-optimization based method.Furthermore,the accurate forecast positions with S-VINS are fed back to assist PPP in GNSS-challenged environments.The statistical analysis of a GNSS outage simulation test shows that the S-VINS mode can effectively suppress the degradation of positioning accuracy compared with the INS-only mode.We also carried out a vehicle-borne experiment collecting multi-sensor data in a GNSS-challenged environment.For the complex driving environment,the PPP positioning capability is significantly improved with the aiding of S-VINS.The 3D positioning accuracy is improved by 49.0%for Global Positioning System(GPS),40.3%for GPS+GLOANSS(Global Navigation Satellite System),45.6%for GPS+BDS(BeiDou navigation satellite System),and 51.2%for GPS+GLONASS+BDS.On this basis,the solution with the semi-tight coupling scheme of multi-GNSS PPP/S-VINS achieves the improvements of 41.8-60.6%in 3D position-ing accuracy compared wit展开更多
文摘基于激光结构光的视觉传感器广泛应用于焊接领域的坡口检测和焊缝跟踪。该文提出了一种基于组合激光结构光的新型视觉传感器,独特的光路结构设计避免了传感器应用于不同焊接位姿时繁琐的外参数标定,仅依靠传感器内部固有参数(应用前需校准)和焊接坡口图像的特征点坐标值,即可实现焊接坡口参数的在线检测,有效增强了传感器的适应性。通过对不同图像处理算法的改进和合理组合,对图像处理流程进行了优化。动态感兴趣区(region of interest,ROI)区域提取算法可快速寻获有价值的激光线和特征点所在区域,有效提升了后续图像处理速度;顶帽变换与自适应二值化组合,在将激光线灰度值均匀化的同时,实现了激光线与背景图像的有效区分。运用基于LOG(Laplacian of Gaussian)算子的边缘识别算法,可提取出激光线的单像素边缘;采用最小二乘法对所求得的激光中心线离散点进行直线拟合,通过联立直线方程求交点的方式,实现了对焊接坡口特征点图像坐标值的准确识别。借助Visual Studio平台,运用改进的图像处理算法、优化的图像处理流程和检测算法,对特征参数不同的V形焊接坡口进行检测试验,检测误差均在±4%以内,验证了所提出视觉传感器及其检测算法和图像处理流程的合理性和适用性。
基金the National Natural Science Foundation of China(Grant No.41774030,Grant 41974027)the Hubei Province Natural Science Foundation of China(Grant No.2018CFA081)+1 种基金the National Youth Thousand Talents Program,the frontier project of basic application from Wuhan science and technology bureau(Grant No.2019010701011395)the Sino-German mobility programme(Grant No.M-0054).
文摘Because of its high-precision,low-cost and easy-operation,Precise Point Positioning(PPP)becomes a potential and attractive positioning technique that can be applied to self-driving cars and drones.However,the reliability and availability of PPP will be significantly degraded in the extremely difficult conditions where Global Navigation Satellite System(GNSS)signals are blocked frequently.Inertial Navigation System(INS)has been integrated with GNSS to ameliorate such situations in the last decades.Recently,the Visual-Inertial Navigation Systems(VINS)with favorable complementary characteristics is demonstrated to realize a more stable and accurate local position estimation than the INS-only.Nevertheless,the system still must rely on the global positions to eliminate the accumulated errors.In this contribution,we present a semi-tight coupling framework of multi-GNSS PPP and Stereo VINS(S-VINS),which achieves the bidirectional location transfer and sharing in two separate navigation systems.In our approach,the local positions,produced by S-VINS are integrated with multi-GNSS PPP through a graph-optimization based method.Furthermore,the accurate forecast positions with S-VINS are fed back to assist PPP in GNSS-challenged environments.The statistical analysis of a GNSS outage simulation test shows that the S-VINS mode can effectively suppress the degradation of positioning accuracy compared with the INS-only mode.We also carried out a vehicle-borne experiment collecting multi-sensor data in a GNSS-challenged environment.For the complex driving environment,the PPP positioning capability is significantly improved with the aiding of S-VINS.The 3D positioning accuracy is improved by 49.0%for Global Positioning System(GPS),40.3%for GPS+GLOANSS(Global Navigation Satellite System),45.6%for GPS+BDS(BeiDou navigation satellite System),and 51.2%for GPS+GLONASS+BDS.On this basis,the solution with the semi-tight coupling scheme of multi-GNSS PPP/S-VINS achieves the improvements of 41.8-60.6%in 3D position-ing accuracy compared wit
