Sub-meter vehicle navigation using efficient pre-mapped visual landmarks

Han-Pang Chiu, Mikhail Sizintsev, Xun S. Zhou, Phillip Miller, Supun Samarasekera, Rakesh Kumar

Abstract: This paper presents a vehicle navigation system that is capable of achieving sub-meter GPS-denied navigation accuracy in large-scale urban environments, using pre-mapped visual landmarks. Our navigation system tightly couples IMU data with local feature track measurements, and fuses each observation of a pre-mapped visual landmark as a single global measurement. This approach propagates precise 3D global pose estimates for longer periods. Our mapping pipeline leverages a dual-layer architecture to construct high-quality pre-mapped visual landmarks in real time. Experimental results demonstrate that our approach provides sub-meter GPS-denied navigation solutions in large-scale urban scenarios.