  This paper advances the field of pedestrian localization by introducing a
unifying framework for opportunistic positioning based on nonlinear factor
graph optimization. While many existing approaches assume constant availability
of one or multiple sensing signals, our methodology employs IMU-based
pedestrian inertial navigation as the backbone for sensor fusion,
opportunistically integrating Ultra-Wideband (UWB), Bluetooth Low Energy (BLE),
and WiFi signals when they are available in the environment. The proposed
PEOPLEx framework is designed to incorporate sensing data as it becomes
available, operating without any prior knowledge about the environment (e.g.
anchor locations, radio frequency maps, etc.). Our contributions are twofold:
1) we introduce an opportunistic multi-sensor and real-time pedestrian
positioning framework fusing the available sensor measurements; 2) we develop
novel factors for adaptive scaling and coarse loop closures, significantly
improving the precision of indoor positioning. Experimental validation confirms
that our approach achieves accurate localization estimates in real indoor
scenarios using commercial smartphones.
