To address the potential limitations of the voxel-oriented training strategy and extend its applicability to dynamic environments or real-time performance, the following approaches can be considered:
Dynamic Voxel Adaptation: Implementing a dynamic voxel adaptation mechanism that adjusts the voxel size or resolution based on the local scene complexity or motion dynamics can enhance the method's adaptability to dynamic environments. This can help in capturing both static and moving objects effectively.
Temporal Fusion: Introducing temporal fusion techniques to incorporate information from previous frames and maintain consistency in the mapping process. By fusing information over time, the method can handle dynamic changes in the environment and ensure continuous mapping updates.
Efficient Memory Management: Optimizing memory management strategies to efficiently store and update historical data within the sliding window. Implementing memory-efficient data structures and compression techniques can reduce memory overhead and enable real-time performance in large-scale environments.
Parallel Processing: Leveraging parallel processing capabilities to accelerate voxel-oriented training and hierarchical sampling. Distributing computation tasks across multiple cores or GPUs can speed up the mapping process and improve efficiency in handling dynamic scenes.
Adaptive Sampling Policies: Introducing adaptive sampling policies that prioritize regions with high information gain or dynamic changes can enhance the method's responsiveness to real-time updates. By dynamically adjusting the sampling strategy, the method can focus on relevant areas for accurate mapping.