An Enhanced GNSS/MINS Integrated Navigation System With a Wheel Velocity Predictor Based on Vehicle Dynamics Model
Wei Huang, Zhiqin Zhao, Xiaozhang Zhu, Shiwen Lei
Abstract
The GNSS/INS integrated navigation system is widely used in intelligent vehicles. Due to the vulnerability of GNSS and the low accuracy of low-cost inertial devices, the performance of dead reckoning during GNSS outages is of great concern. The non-holonomic constraint (NHC) is a widely used method to improve dead reckoning performance, but it ignores the sideslip effect caused by tire deformation, which leads to performance degradation. The conventional vehicle dynamics model can achieve higher accuracy by modeling tire sideslip effects; however, its parameters are often complex and challenging to obtain. This work proposes a novel wheel velocity predictor (WVP) based on the vehicle dynamics model, which models tire sideslip effects while having concise and easy-to-estimate parameters. Based on this model, an integrated navigation system is proposed, which can adaptively estimate WVP parameters when GNSS is available and use the additional information from trained WVP to improve navigation performance during GNSS outages. Notably, the proposed system utilizes a more accurate model than conventional NHC while avoiding requiring additional sensors or complex prior vehicle dynamics model parameters. Field experiments have shown that the proposed scheme can accurately estimate the tire sideslip angle in real-time and significantly improve dead reckoning performance compared with the NHC-based method. Furthermore, as part of the WVP parameters, the inertial measurement unit mounting angles can be estimated even with a non-straight driving trajectory. Compared with the NHC-based method, which requires a straight driving trajectory to converge, the application limitation is significantly relaxed.