Cross Modal Localization

Cross-modal localization aims to pinpoint the location of a sensor (e.g., camera, LiDAR) within a map represented using a different modality (e.g., satellite imagery, point clouds). Current research heavily utilizes deep learning, employing transformer networks and convolutional architectures to bridge the semantic gap between different sensor data types, often incorporating attention mechanisms for improved feature matching and localization accuracy. This field is crucial for advancing autonomous navigation and robotics, enabling more robust and reliable localization in challenging environments where GPS is unavailable or unreliable.