The field of robotics and navigation is witnessing significant developments in sensor model identification and state initialization. Researchers are exploring innovative methods to determine the most likely sensor model for a time series of unknown measurement data, ensuring more accurate and robust integration of new sensor elements. The focus is on developing simplified integration of sensor modalities to downstream applications, circumventing common pitfalls in the usage and development of localization approaches. Notably, novel strategies are being proposed to improve the initialization of sensorized platforms, delaying the use of global measurements until sufficient information is available. Additionally, optimal alignment methods are being developed for Lorentz orientation and matrix Lie groups, extending existing methods to indefinite metrics. These advancements have the potential to enhance the performance and reliability of navigation systems. Noteworthy papers include: Sensor Model Identification via Simultaneous Model Selection and State Variable Determination, which introduces a health metric to verify the outcome of the selection process. GNSS-inertial state initialization by distance residuals, which proposes a novel initialization strategy that delays the use of global GNSS measurements. Optimal alignment of Lorentz orientation and generalization to matrix Lie groups, which outlines a conceptually simple method for aligning 4-vectors in indefinite metrics. Real-Time Initialization of Unknown Anchors for UWB-aided Navigation, which presents a framework for automatic detection and calibration of unknown UWB anchors during operation.