Analysis for vehicle technology about 10_nlos_correction_ HOT 9 CLOSED

Fig. 11. GNSS/LiDAR integration final result: GNSS positioning based on WLS-NE without covariance estimation. The small circles represent the pose graph for further graph optimization. The colored points indicate the point clouds from the graph SLAM.

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Fig. 12. GNSS/LiDAR integration final result: GNSS positioning based on WLS-NE with covariance estimation. The small circles represent the pose graph for further graph optimization. The colored points indicate the point clouds from the graph SLAM. The GNSS is integrated with LiDAR only when the covariance of GNSS positioning is less than 20 meters.

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Fig. 7. Experimental results of WLS and WLS -NE, which depicted in red and blue dots, respectively. Top panel indicates the numbers of satellites used. Button panels indicates the 3D positioning errors.

    #rows = zip(self.GNSSTimeProp,self.SatNum,self.SatNumProp,self.error,self.errorProp,self.Covariance,self.CovarianceFixed)

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Fig. 8. Satellite numbers in top panel. Covariance estimation in button panel: The conventional covariance estimation is indicated in red dots. The proposed covariance estimation is indicated in blue dots. The GNSS positioning error using the WLS-NE is represented in black dots (ground truth for covariance).

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Fig. 13. Conventional GNSS/LiDAR integration positioning error: GNSS positioning based on WLS-NE without covariance estimation. X axis indicates the time, y axis indicates the error in meters.

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Fig. 14. Conventional GNSS/LiDAR integration positioning error: GNSS positioning based on WLS-NE with covariance estimation. X axis indicates the time, y axis indicates the error in meters.

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Fig. 9. Trajectory of the autonomous vehicle is indicated by red curve. The yellow arrows indicate the GNSS positioning result.

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for introduction:

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larger scenarios

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