To extend the proposed efficient greedy algorithm for dynamic sensor configurations, several modifications can be implemented. First, the algorithm could incorporate a mechanism for real-time monitoring of sensor status, allowing it to detect when an IMU is added or removed. This would involve maintaining a dynamic list of active IMUs and their corresponding measurements.
When a new IMU is added, the algorithm should re-evaluate the utility of existing measurements and potentially include the new IMU's data in the subset selection process. This could be achieved by recalculating the Fisher information matrix for the updated set of measurements, but only for the new IMU and its immediate neighbors to minimize computational overhead.
Conversely, if an IMU is removed, the algorithm should efficiently update the measurement subset by excluding the removed IMU's data and recalibrating the remaining IMUs. The recalibration could leverage the previously computed Jacobians and Fisher information matrices to avoid redundant calculations, thus maintaining the efficiency of the greedy approach.
Additionally, the algorithm could implement a threshold for utility that adapts based on the number of active IMUs, ensuring that the selection process remains robust even as the sensor configuration changes. This adaptability would allow the algorithm to maintain high calibration accuracy and efficiency in dynamic environments, such as mobile robotics or autonomous vehicles, where sensor configurations frequently change due to operational requirements.