Learning-Based Data Fusion With Multitask Odometry Network for Robust and High-Precision Vehicle Positioning

Robust and high-precision vehicle positioning information is crucial for Internet of Things (IoT) applications like autonomous driving and intelligent transportation. While global navigation satellite system (GNSS)/inertial navigation system (INS) integration ensures reliable positioning in open areas, signal interruptions in urban environments cause rapidly accumulating INS errors. Deep neural networks (DNNs) are integrated into the Kalman filter (KF) framework to mitigate INS errors through learning-based data fusion. If the DNN provides unreliable pseudo-measurement information, the positioning results may deteriorate. To enhance DNN estimation reliability, this study introduces a 1-D convolution-based spatiotemporal attention mechanism for implicit modeling of temporal dependencies and spatial correlations in sensor data. This mechanism is utilized to construct a multitask odometry network (MT-ONet) for accurate forward velocity and motion state estimation of vehicles. Building upon this, an adaptive fusion vehicle positioning algorithm is proposed. This algorithm combines the MT-ONet and an improved adaptive KF (AKF) to dynamically adjust measurement noise to enhance robustness. Experimental results indicate that MT-ONet offers high estimation accuracy and low computational complexity, making it suitable for deployment on resource-constrained IoT devices. During simulated 180-s GNSS outages, the proposed method decreases horizontal root mean square (RMS) and maximum errors by 14.47% and 16.09% compared to hardware odometer-assisted scheme, and by 33.88% and 40.28% versus the nonholonomic constraint (NHC)-assisted scheme.