Map Compression

Map compression research focuses on efficiently representing spatial data for various applications, primarily aiming to reduce storage and communication costs while preserving essential information. Current efforts explore techniques like discrete cosine transforms (DCT) for data pre-processing, locality-sensitive hashing (LSH) for nearest-neighbor searches, and tensor train (TT) decomposition of signed distance functions (SDFs) for 3D scene representation. These advancements are crucial for improving the efficiency of tasks such as autonomous navigation, multi-agent exploration, and high-definition map construction in resource-constrained environments, ultimately impacting robotics, remote sensing, and machine learning.