An LSTM-Improved NHC in Tightly Coupled GNSS PPP/INS/ODO System for Robust Vehicle Navigation
zemin wang, Shujie Zhou, Xinye Dai, Chunqi Dai, Jingchen Xiong, Shengfeng Gu · IEEE Transactions on Intelligent Transportation Systems · 2025
Intelligent vehicle (IV) navigation is a key component of intelligent transportation systems (ITS). To enhance their performance in positioning, navigation and timing (PNT), usually inputs from the exteroceptive sensors, e.g., camera (images) or light detection and ranging (LiDAR) (point clouds), can be used to identify the environment and objects, or help other sensors with signal quality assessment. However, the measurements from the interoceptive sensors, e.g., inertial measurement unit (IMU) and wheel odometer (ODO), are rarely considered, while they are more resilient to the environment changes and commonly equipped within the modern vehicles. Integrating the global navigation satellite system (GNSS) precise point positioning (PPP) algorithm and centered on the inertial navigation system (INS), this paper proposes a tightly coupled (TC) GNSS PPP/INS/ODO integration algorithm that utilizes long short-term memory (LSTM) to predict non-holonomic constraint (NHC) components. To further assess the online prediction quality without ground truth, a novel cross-validation method is proposed, also to adjust the corresponding measurement variance. Results of field tests show that, compared with traditional GNSS PPP/INS/ODO system, the proposed algorithm can achieve an average root mean square (RMS) improvement of (16.05%, 16.03%, 16.06%) and a maximum (MAX) improvement of (22.10%, 27.57%, 29.90%) in positioning along the north, east and down (NED) directions. More importantly, the proposed method can effectively mitigate the degradation in positioning performance caused by erroneous constraints under complex motion conditions.