To further improve the effectively constrained global bundle adjustment strategy and leverage NeRF's implicit loop closure capabilities more effectively, several enhancements can be considered:
Dynamic Keyframe Selection: Instead of relying solely on distance and parallax angle criteria for keyframe selection, incorporating dynamic criteria based on scene complexity or feature richness can help in identifying keyframes that are more relevant for loop closure detection.
Adaptive Sampling: Implementing an adaptive sampling strategy that prioritizes sampling in regions with high uncertainty or where loop closures are likely to occur can improve the effectiveness of the global bundle adjustment.
Temporal Consistency: Introducing a mechanism to maintain temporal consistency in keyframe selection and loop closure detection can enhance the system's ability to detect and correct loop closures effectively.
Feature-based Constraints: Utilizing feature-based constraints in addition to pixel constraints can provide additional information for loop closure detection and pose optimization, further enhancing the system's accuracy.
Multi-level Optimization: Implementing a multi-level optimization approach that considers both local and global constraints simultaneously can help in refining the pose estimation and loop closure correction process.