Example Concept: Inside-out tracking relies on a combination of visual-inertial odometry and machine learning algorithms to interpret sensor data from cameras and IMUs. By enhancing the robustness of these algorithms, developers can improve tracking accuracy. Techniques such as dynamic feature detection, adaptive filtering, and predictive modeling help maintain stable tracking by compensating for rapid movements and complex environmental changes.