|Abstract:||For the higher level of automation, accuracy of navigation states estimation is crucial demand. To achieve full and reliable navigation evaluation, trustable reference system needs to be developed. Although the presence of reference system of an inertial measurement unit (IMU) with global navigation satellite system (GNSS) through the multi-sensor fusion scheme was integrated, but in GNSS-denied or challenging environment, the navigation solution could not be accurately estimated and still needs to be fixed. This paper proposes new strategies for better estimating the LiDAR-based position uncertainty and updating the reference system. The first strategy involves determining the appropriate position error covariance matrix based on the Hessian matrix and the scale of covariance obtained from normal distribution transform (NDT) scan matching technique, and the geometric dilution of precision (GDOP) computed from the distribution of point cloud segments in each scan. In the second proposed strategy, the updated reference system was post-processed according to the loosely coupled INS/GNSS/NDT integration scheme with the forward and backward smoothing process. Preliminary results indicate that the updated reference system obtained from proposed strategy not only provides more reliable navigation estimation compared to an existing reference system from commercial software but also can be used for accurate evaluation of positioning, navigation and timing (PNT) with automated vehicle applications.|
Proceedings of the 35th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2022)
September 19 - 23, 2022
Hyatt Regency Denver
|Pages:||1650 - 1664|
|Cite this article:||
Srinara, Surachet, Chiu, Yu-Ting, "Adaptive Covariance Estimation of LiDARbased Positioning Error for Multi-Sensor Fusion Scheme with Autonomous Vehicular Navigation System," Proceedings of the 35th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2022), Denver, Colorado, September 2022, pp. 1650-1664.
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