This paper presents PySkyLumos, an open-source Python framework for simulating skylight polarization measurements and metadata. It implements the Rayleigh, Berry, and Pan models, capturing both classical scattering and elevation-expressed singularity dynamics. Efficiency benchmarks show a fivefold overall speedup and up to two orders of magnitude faster Stokes-parameter image formation compared to existing tools. Validation against real polarization camera data confirms its accuracy and suitability for bio-inspired computer vision, navigation, robotics, and sensing applications.

Quantum cold-atom sensors provide precise measurements of gravitational acceleration and gravity gradients. By matching these measurements to a high-resolution gravity database, a moving platform can derive its position using map-matching techniques that fuse gradient observations with inertial navigation. One such fusion technique, particle filters, is dominated by the cost of evaluating gravity gradients via surface integrals at each location. To overcome this overhead, we introduce a deep-learning model that predicts the vertical gravity gradient from a compact subset of local gravity anomaly samples, eliminating the need for full integral computations. We integrate this deep neural network into the map-matching framework, benchmark its accuracy against conventional methods, and demonstrate its real-time performance within a simulated inertial navigation system driven by a quantum sensor model.

This paper presents PySkyLumos, an open-source Python framework for simulating skylight polarization measurements and metadata. It implements the Rayleigh, Berry, and Pan models, covering single scattering, the splitting of the neutral points caused by multiple scattering, and the elevation-dependent motion of these points. Benchmarks show a fivefold overall speed-up and up to two orders of magnitude faster Stokes-parameter image formation than a recent open-source simulator. Validation against real polarization-camera images shows close agreement between the simulated and measured polarization patterns, supporting its use in algorithm development for bio-inspired computer vision, navigation, robotics, and sensing.