首页|Studies in the Area of Robotics Reported from Hunan University (A Novel Uwb/imu/odometer-based Robot Localization System In Los/nlos Mixed Environments)
Studies in the Area of Robotics Reported from Hunan University (A Novel Uwb/imu/odometer-based Robot Localization System In Los/nlos Mixed Environments)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News - Current study results on Robotics have been published. According to news reportingoriginating in Shenzhen, People’s R epublic of China, by NewsRx journalists, research stated, “The accuracyof exist ing ultra-wideband (UWB) range-based indoor localization methods is generally de graded due tothe non-line-of-sight (NLOS) situations where a serve bias in UWB range measurements is unavoidable. Inthis article, we first propose a two-stage NLOS detection method to detect line-of-sight (LOS)-measureddistances in mixed LOS/NLOS indoor environments.”Financial support for this research came from National Natural Science Foundatio n of China (NSFC).The news reporters obtained a quote from the research from Hunan University, “Th en, a high-accuracyUWB/IMU/Odometer integrated localization system is presented using an adaptive multi-algorithm localizationframework based on the number of detected LOS-measured distances. Specifically, under conditionsof one or two L OS-measured distances, an improved adaptive EKF positioning algorithm (IAEKF) is proposed.Compared with the traditional extended Kalman filter (EKF)-based fusi on scheme, the weightfunction of innovation is exploited to adaptively estimate the measurement noise covariance matricesand further reduce the influence of t he changing measurement noise in NLOS conditions. For three ormore LOS-measured ranges, a novel tightly coupled fusion factor graph framework is developed. To furtherimprove the performance of seamless positioning in transaction areas, a strong constraint of trajectorysmoothness is designed and added to the factor g raph framework using the weight value of IMU/odometermeasurements.”
ShenzhenPeople’s Republic of ChinaAsiaEmerging TechnologiesMachine LearningRobotRoboticsHunan University
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