This chapter explores mapping, localization, and navigation techniques in robotics, highlighting the role of deep learning in processing high-dimensional sensor data. It discusses various mapping representations - geometric, voxel-based, and semantic - with models like CLIP and NeRF. Additionally, it covers localization methods, diffusion-based navigation strategies like Mobility VLA, and advances in hierarchical learning that improve robot adaptability in complex environments.