Inertial navigation and WiFi are two common approaches for pedestrian localization. However, conventional pedestrian dead reckoning (PDR) and WiFi fingerprinting suffer from limited adaptability to different users and poor robustness to environmental changes, respectively. Recent deep-learning-based methods address pedestrian localization by modeling sequential dependencies in inertial data, but they typically rely on a single inertial measurement unit (IMU), which is insufficient to capture the spatial correlations of human skeletal motion. In parallel, the Fine Time Measurement (FTM) procedure in IEEE 802.11mc enables round-trip time (RTT)–based ranging and localization, yet the coordinates of WiFi routers still require labor-intensive prior surveying, limiting deployment flexibility. This paper presents a pedestrian and router colocalization framework that jointly estimates pedestrian trajectories and WiFi router positions. The proposed framework employs multiple body-worn IMUs and a long short-term memory (LSTM) network to learn both spatial and temporal dependencies in human motion, thereby enabling velocity dead reckoning (VDR). The VDR-estimated pedestrian velocity is then fused with WiFi RTT measurements through factor graph optimization (FGO), in which both pedestrian and router coordinates are treated as unknown variables. Experimental results demonstrate that the multi-IMU-based VDR effectively models pedestrian velocity, while WiFi RTT ranging constrains the long-term drift of VDR. The combined VDR/WiFi RTT framework achieves meter-level positioning accuracy in both indoor and outdoor environments, without requiring pre-surveyed router coordinates, and thus provides a promising solution for pedestrian localization in the Internet of Things (IoT) era.