Accurate sensor calibration is a critical challenge in the development of automated vehicles, especially in dynamic and modular sensor configurations. Traditional target-based methods, while precise, are limited in scalability and adaptability. In this work, we propose a modular, targetless, ego-motion-based calibration framework for multi-modal sensors, including a monocular camera, LiDAR, and 4D radar. The framework leverages odometry trajectories for extrinsic calibration, incorporating temporal alignment, trajectory scaling, and both pairwise and joint optimization techniques to achieve robust and accurate sensor alignment. Experimental validation using the View-of-Delft (VoD) dataset demonstrates the framework’s
robustness across diverse sensor setups, adaptability to real-world conditions. Our results underscore the potential of scalable, targetless calibration approaches to enhance the reliability and flexibility of automated systems, supporting implementation in real-world scenarios.