Semantic Grid

Semantic grids represent the environment as a structured grid of semantic labels, offering a concise and computationally efficient scene representation for autonomous systems. Current research focuses on improving the accuracy and robustness of semantic grid generation, particularly through multi-sensor fusion (combining camera and LiDAR data) and the incorporation of temporal information to handle occlusions and predict scene evolution. These advancements are crucial for enabling reliable perception and planning in autonomous driving, robotics, and other applications requiring accurate environmental understanding. Model architectures often leverage deep learning techniques, including convolutional neural networks and attention mechanisms, to process sensor data and generate accurate semantic grids in real-time.