Inside GNSS Media & Research

JUL-AUG 2019

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54 Inside GNSS J U L Y / A U G U S T 2 0 1 9 the NLOS measurement is identified and corrected using the NLOS error model. e evaluated results show that the pro- posed method can improve GNSS posi- tioning accuracy compared to that of WLS. Table III is given to compare the pro- posed perceived environment aided GNSS with different state-of-the-art GNSS urban positioning methods. e methods in the first three rows make use of 3-D mapping database to improve the positioning method. Hence, all of them require the prior information of the receiver's location to provide accurate GNSS positioning results. e proposed method requires extra sensor comparing to the 3DMA GNSS. However, these sen- sors are existing in most of the autono- mous driving vehicles. In addition, the perception on obstacles detection is also used for the purpose online path and motion planning. e computation load for the proposed GNSS SPP is similar to that of the conventional WLS. Last but not the least, the LiDAR can provide the lateral distance which can be used to cor- rect NLOS affected pseudorange mea- surement. erefore, the HDOP remains unchanged for the proposed GNSS SPP. e goal of this article is to raise the awareness of the perceived environment by LiDAR or camera can be used to aid positioning because the positioning sen- sors could be affected by the surrounding environments. Other than this demon- strated LiDAR-aided GNSS SPP, there are several setups that can use the same idea. For example, assembling the images collected from 360 degree cameras to describe the obstacles in the skyplot repre- sentation. is skyplot with obstacles can be used to identify GNSS NLOS measure- ment. is perceived environment aided idea can also apply to aid LiDAR posi- tioning based on the perceived obstacles by image processing. We will work on the research of the dynamic objects removal for LiDAR positioning in the near future. Acknowledgments e authors acknowledge the support of Hong Kong PolyU startup fund on the project 1-ZVKZ, "Navigation for Autonomous Driving Vehicle using Sensor Integration." FIGURE 13 The performance comparison between the conventional WLS and the proposed method (WLS-NC). The reference trajectory is provided by the RTK GNSS/INS integrated system. FIGURE 14 Performance comparison between the conventional WLS and the proposed method (WLS- NC). Only the data epochs with NLOS correction (it's about 65% of the overall data.) are compared and shown. METHODS 3-D MAPS EXTRA SENSORS NLOS CORRECTION PRIOR INFORMATION HDOP COMPUTATION LOAD Satellite Visibility Prediction √ × Exclusion √ Increase Low GNSS Shadow Matching √ × Do not use pseudorange √ Do not use pseudorange Low Ray-Tracing based 3DMA GNSS √ × Correction √ Not changed High Perceived Environment Aided GNSS Single Point Positioning × "√" * Correction × Not changed Low^ * They are existing sensors in most of the autonomous driving vehicles. ^ The LiDAR based object detection is not considered as computation load for the proposed GNSS SPP since the object detection is essential and existed in autonomous driving vehicles. TABLE III COMPARISON BETWEEN DIFFERENT STATE-OF-THE-ART METHODS SINGLE POINT POSITIONING

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