Sensor Alignment

Sensor alignment focuses on accurately determining the relative positions and orientations of multiple sensors within a system, crucial for reliable data fusion and accurate estimations. Current research emphasizes robust methods to handle sensor misalignment, employing techniques like neural networks (e.g., recurrent inertial graph-based estimators), multi-task learning, and Kalman filtering variants (e.g., Unscented Kalman Filter, Invariant Extended Kalman Filter) to mitigate errors caused by imperfect sensor placement. These advancements are vital for improving the performance of various applications, including autonomous vehicle navigation, human motion tracking, and remote sensing, where precise sensor data integration is essential for accurate and reliable results.