Road-Pulse: Pavement Vibration Features from Accelerator to Enhance Intelligent Vehicle Localization

Speed bumps and expansion joints widely available on pavements cause pulse-like vibrations that can be measured by an in-vehicle accelerator. Such vibration patterns are called road-pulses in this paper, and we proposed a sliding Gaussian model (SGM) for detection. The detected road-pulses are associated with accurate positions and image features for road-pulse map construction. In the localization step, road-pulse patterns are detected and matched with the road-pulse map. And the corresponding image features and positions stored in the map allow accurate vehicle localization afterward. The proposed road-pulse-based localization is integrated into a downward pavement visual odometry with a Kalman filter based on the second-order Markov model (KF-MM2) for enhancing vehicle localization. The proposed method was validated on two different pavement conditions. Experimental results demonstrate that the proposed method can greatly improve vehicle localization with low cost and high feasibility.