The grid-based local fusion approach in LGSDF, while effective in capturing local details and updating the implicit ESDF map, may have some potential limitations:
Limited Spatial Resolution: Grid-based representations may struggle to capture fine details in complex and heterogeneous environments, especially in regions with sharp changes in geometry or where objects are densely packed.
Grid Boundary Artifacts: Grid-based fusion can introduce artifacts at grid boundaries, leading to inaccuracies in the estimated signed distances, particularly in regions where multiple grids intersect.
Memory and Computational Overhead: Maintaining and updating a large number of grids can increase memory and computational requirements, especially in scenarios with a high density of observations or dynamic changes.
To improve the grid-based local fusion approach in LGSDF for handling more complex environments, the following strategies can be considered:
Adaptive Grid Refinement: Implementing adaptive grid refinement techniques can dynamically adjust the grid resolution based on the complexity of the scene, focusing computational resources where they are most needed.
Local Feature Integration: Incorporating local feature extraction methods, such as edge detection or corner detection, can enhance the grid-based fusion process by capturing important geometric details in the environment.
Multi-Resolution Grids: Utilizing multi-resolution grids can provide a balance between capturing fine details and maintaining global context, allowing for more efficient representation of complex and heterogeneous scenes.
Contextual Information Integration: Integrating contextual information from semantic segmentation or object detection can help refine the grid-based fusion process by providing additional insights into the scene structure and dynamics.
By addressing these limitations and implementing these improvements, the grid-based local fusion approach in LGSDF can be enhanced to handle more complex and heterogeneous environments effectively.