Example Concept: In multi-sensor navigation, error propagation refers to the way measurement inaccuracies from sensors like IMUs, GPS, and cameras combine and affect the final navigation solution. Each sensor contributes its own error characteristics, such as bias, noise, and drift. These errors can be compounded through data fusion processes, such as Kalman filtering or SLAM, leading to increased uncertainty in the robot's estimated state. Effective error modeling and sensor fusion strategies are essential to minimize the impact of these errors, ensuring robust and accurate navigation.