To extend the EF-Calib framework for multi-camera systems involving more than two cameras, several modifications and enhancements would be necessary. First, the calibration framework would need to incorporate a more complex model for the extrinsic parameters, allowing for the estimation of relative poses between multiple cameras. This could involve the use of a graph-based optimization approach, where each camera is treated as a node and the relative transformations between them as edges.
Additionally, the continuous-time trajectory representation could be adapted to accommodate multiple cameras by introducing a shared temporal reference or synchronization mechanism that aligns the event streams and frame captures from all cameras. This would require a robust method for handling time offsets not just between pairs of cameras but across the entire system, potentially leveraging the asynchronous nature of event cameras to create a unified timeline.
Moreover, the calibration pattern could be designed to include features that are easily detectable by all camera types involved, ensuring that each camera can contribute to the calibration process. The feature extraction and refinement stages would also need to be enhanced to handle the increased complexity of data from multiple cameras, possibly by employing advanced machine learning techniques to improve feature recognition and reduce noise.
Finally, extensive testing and validation would be essential to ensure that the extended EF-Calib framework maintains high accuracy and robustness across various configurations and environments, similar to the performance demonstrated in the original two-camera setup.