Consistent Map

Consistent map creation aims to build accurate and reliable spatial representations of environments, crucial for robotics and autonomous systems. Current research focuses on improving global consistency using various techniques, including graph-based optimization, implicit neural representations (like point-based or octree-based models), and integration of diverse sensor data (LiDAR, RGB-D cameras, radar) with prior map information. These advancements enhance the robustness and accuracy of mapping, particularly in challenging GPS-denied environments, and enable applications such as long-term autonomous navigation and collaborative multi-robot mapping. The development of efficient and scalable algorithms for consistent map generation is driving progress in several fields, including computer vision, robotics, and autonomous driving.