Robust and globally-referenced positioning is indispensable for autonomous driving vehicles. Global navigation satellite system (GNSS) is still an irreplaceable sensor. Satisfactory accuracy (about 1 m) can be obtained in sparse areas. However, the GNSS positioning error can be up to 100 m in dense urban areas due to the multipath effects and non-line-of-sight (NLOS) receptions caused by reflection and blockage from buildings. NLOS is currently the dominant factor degrading the performance of GNSS positioning. Recently, the camera has been employed to detect the NLOS and then to exclude the NLOS measurements from GNSS calculation. The exclusion of NLOS measurements can cause severe distortion of satellite distribution, due to the excessive NLOS receptions in deep urban canyons. Correcting the NLOS receptions with the aid of 3D light detection and ranging after detection of NLOS receptions using a fish-eye camera was proposed in this study. Finally, the GNSS positioning was improved by using the healthy and corrected NLOS pseudo-range measurements. The proposed method is evaluated through real road tests in typical highly urbanised canyons of Hong Kong. The evaluation results show that the proposed method can effectively improve the positioning performance.
Using Sky-pointing fish-eye camera and LiDAR to aid GNSS single-point positioning in urban canyons
IET Intelligent Transport Systems ; 14 , 8 ; 908-914
2020-05-29
7 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
globally-referenced positioning , typical highly urbanised canyons , satisfactory accuracy , Sky-pointing fish-eye camera , aid GNSS single-point positioning , 3D light detection , NLOS measurements , NLOS pseudorange measurements , size 1.0 m , dense urban areas , optical radar , global navigation satellite system , size 100.0 m , Global Positioning System , GNSS positioning error , satellite navigation , positioning performance , irreplaceable sensor , sparse areas , excessive NLOS receptions , GNSS calculation , cameras , deep urban canyons , nonline-of-sight receptions , autonomous driving vehicles , satellite distribution , dominant factor degrading
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