Mammals have the ability to perform accurate and robust path integration-based metric navigation even in the absence of visual or other environmental cues, which provides a new idea to find brain-inspired solutions to tackle the problems of serious drift of the inertial measurement unit (IMU)-based inertial navigation of unmanned aerial vehicle (UAV) when the external sensory cues are not available. Multiscale grid cells in the medial entorhinal cortex are thought to be a fundamental portion of mammals’ ability to perform 3-D path integration-based metric navigation. This article studies and presents, for the first time, a neural system to implement path integration-based metric navigation in 3-D environments integrating networks of encoding and decoding multiscale grid cells using neural dynamic models, i.e., 3-D continuous attractor network and neural cliques, respectively. Experimental results show that the neural system can successfully path integrate self-motion information for large-scale 3-D navigation and provides robust and error-correcting position information, displaying possible neural solution to overcome serious drift of IMU-based inertial navigation of UAV in the absence of external sensory cues.