The efficient measurement subset selection approach presented in this paper could be beneficial for various sensor modalities beyond inertial measurement units (IMUs). Some notable examples include:
Camera Systems: In visual odometry and simultaneous localization and mapping (SLAM), selecting high-utility frames or keypoints can significantly enhance the efficiency of feature extraction and matching processes. The proposed greedy algorithm could help in identifying the most informative frames for calibration and mapping.
LiDAR Sensors: For applications involving 3D mapping and object detection, selecting subsets of LiDAR scans that provide the most information about the environment can reduce processing time and improve the accuracy of the generated maps.
Rangefinders and Ultrasonic Sensors: In robotics, these sensors are often used for distance measurement. Efficient selection of measurement subsets can optimize the calibration of sensor arrays, particularly in environments with varying levels of noise and interference.
Environmental Sensors: Sensors measuring temperature, humidity, or pressure in dynamic environments could benefit from this approach by selecting the most informative readings for calibration and data fusion, especially in applications like weather monitoring or climate studies.
Multi-Sensor Fusion Systems: In systems that integrate data from various sensors (e.g., IMUs, cameras, LiDAR), the efficient measurement subset selection can streamline the calibration process by focusing on the most informative data from each sensor type, enhancing overall system performance.
By applying the efficient greedy algorithm to these sensor modalities, researchers and engineers can achieve faster calibration times and improved accuracy, making it a versatile tool in the field of sensor fusion and calibration.