Neural Network Aided Factor Graph Optimization for Collaborative Pedestrian Navigation

Indoor navigation and positioning services for pedestrians are challenging because of the lack of satellite signals and the unpredictability of pedestrian motion. The inertial measurement unit (IMU)-based pedestrian dead reckoning (PDR) algorithm can provide continuous position estimation for individual pedestrians. However, the accumulation of errors leads to inaccurate pedestrian position results. Radio signals such as ultra-wideband (UWB) can range between pedestrians and anchors and provide high-precision positioning information; nonetheless, radio positioning requires infrastructure deployment and maintenance in indoor environments, thus limiting the popularization and implementation of these technologies. In this paper, a neural network aided factor graph optimization (NN-FGO) method was proposed for collaborative pedestrian navigation (CPN). It integrates IMU and UWB sensors to implement PDR for individual pedestrians and CPN for the Ad-Hoc network, and it is infrastructure-free since all the sensors are wearable. For a small or sparse network, ranging constraints will be insufficient to implement an acceptable CPN. A neural network model was suggested for human activity recognition and position loopback detection, which provide virtual constraints for pedestrians. For the heterogeneous problem caused by multiple collaborative signals and constraints, FGO was employed to solve the motion states of multi-pedestrians and multi-epochs. The real experimental results revealed that NN-FGO can provide 92% accuracy in activity classification. Compared with the extended Kalman filter based CPN, the average position error decreased by 19.6% and 16.0% with triangular and parallel straight geometries, respectively.