Onboard Sensor

Onboard sensors are crucial for enabling autonomous functionality in various platforms, from UAVs and autonomous vehicles to robots and spacecraft. Current research focuses on improving the accuracy and robustness of onboard sensor data processing through advanced algorithms like Kalman filters, Bayesian filters (including TPMBM), and deep learning models (e.g., convolutional neural networks and recurrent neural networks), often integrating data from multiple sensor modalities for enhanced performance. This work is driving advancements in areas such as precise navigation, environmental mapping (SLAM), and real-time decision-making, with significant implications for autonomous systems across diverse applications.