Indoor Integrated Navigation on PDR/Wi-Fi/Barometer via Factor Graph With Local Attention

Factor graph (FG) optimization is increasingly used in the research of multisensor integrated navigation and positioning. Unlike popular Kalman filter algorithms, all states of the FG are optimized holistically. Maximum posterior probability (MAP) estimation is the core of FG models. However, when sensors are subjected to sudden environmental interference, the outputs may deviate significantly from the expected values or even be missing. Some existing FG models lack the ability to identify such interference. Therefore, an FG model with local attention (FGLA) is formally proposed in this article. FGLA constrains the factor nodes in the FG while optimizing the objective function, so that local outliers can be quickly corrected. To quickly optimize the constrained FGs in a non-vector space, an algorithm called bounce gradient descent (BGD) is proposed. We use real and simulation integrated navigation datasets consist of pedestrian dead reckoning (PDR)/Wi-Fi/barometer measurement data to verify the performance of the two proposed algorithms. Experimental results show that FGLA has stronger anti-interference capabilities when facing sudden data exceptions, compared with either the extended Kalman filter (EKF) or the existing FG models. Results also demonstrate that BGD has a faster optimization speed than the projected gradient algorithm.