The SLIM system can be enhanced by integrating semantic information from visual perception through a multi-modal sensor fusion approach. By incorporating data from cameras alongside LiDAR, the system can leverage semantic segmentation to identify and classify objects within the urban environment, such as pedestrians, vehicles, and traffic signs. This additional layer of information can improve the mapping accuracy and robustness, particularly in complex urban settings where traditional geometric features may be insufficient.
To implement this, the SLIM system could utilize deep learning models for real-time semantic segmentation, which would process visual data to extract meaningful features. These features could then be fused with the existing line and plane representations, enriching the map with semantic labels. For instance, the system could create a hybrid map that combines geometric structures (lines and planes) with semantic annotations, allowing for more informed decision-making during navigation and localization tasks.
Moreover, the integration of semantic information could facilitate improved loop closure detection by providing additional context for place recognition, thus enhancing the overall consistency of the map. The system could also employ a multi-task learning framework, where the visual perception model is trained simultaneously with the mapping algorithms, ensuring that both components benefit from shared knowledge and improve their performance collectively.