The adaptive skip mechanism in Skip-SCAR could be enhanced for dynamic environments and multi-floor scenarios by integrating real-time environmental change detection and multi-layer semantic mapping. In dynamic environments, the agent could utilize temporal data analysis to identify significant changes in the scene, such as moving objects or altered layouts. This could involve implementing a recurrent neural network (RNN) or a temporal convolutional network (TCN) to analyze sequences of depth readings and RGB images, allowing the agent to adaptively decide when to skip semantic segmentation based on the detected changes.
For multi-floor scenarios, the adaptive skip mechanism could be improved by incorporating a hierarchical mapping approach. This would involve maintaining separate semantic maps for each floor and utilizing a 3D spatial understanding to determine when the agent transitions between levels. The mechanism could leverage depth information to assess the vertical distance and changes in the environment, allowing the agent to skip unnecessary segmentation steps when moving between floors, provided that the environment remains consistent. Additionally, integrating a multi-modal sensor fusion approach could enhance the agent's ability to perceive and adapt to changes across different floors, ensuring that the adaptive skip mechanism remains effective in complex, dynamic settings.