Development and evaluation of GNSS/INS data processing software for position and orientation systems

Abstract

Currently there are limited supportive and reliable commercial software packages for GNSS/INS data processing. The shortage of compatible GNSS/INS data processing software has become a bottleneck in the development of position and orientation systems (POS) for survey and mapping applications. Thus this paper introduces a GNSS/INS data processing software called Cinertial, which has been recently developed in China, with open definitions of data format and parameters. The algorithm design of the software is described in detail, including the realisation of INS mechanisation, Kalman filtering, and backward smoothing. The developed software is tested and evaluated by comparing it with mainstream commercial software through processing the same field test datasets. A precise and feasible comparison procedure is proposed in the paper, so as to evaluate the quality of any new POS software in an efficient, convenient, and cost-effective way. The procedure was applied to compare our new software with two well known commercial software packages through processing airborne and terrestrial datasets respectively. The results show that the new software can achieve the same level of accuracy as the current commercial software on the market, which reflects a new progress of the POS development in China.

Keywords:

• GNSS/INS
• Kalman filtering
• Position and orientation system (POS)
• Post-processing software

Acknowledgements

This work was supported by the National Natural Science Foundation of China (no. 41174028), the National High Technology Research and Develop Program of China (no. 2012AA12A202, no. 2012AA12A206), the National Natural Science Foundation of China (no. 41231174), and the Open Research Fund of The Academy of Satellite Application. Dr Zhiqin Zhu from the School of Geodesy and Geomatics at Wuhan University is thanked for providing the reference system SPAN-FSAS with relevant cooperation. Qijin Chen and Yahao Cheng are acknowledged for collecting the land test datasets used in our paper.