A Novel Visual Measurement Framework for Land Vehicle Positioning Based on Multimodule Cascaded Deep Neural Network
Zhiyong Zheng, Xu Li, Zhengliang Sun, Xiang Song
Abstract
This article proposes a novel visual measurement framework, multimodule cascaded deep neural network (MMC-DNN), to achieve accurate, reliable, and cost-effective vehicle positioning in complex urban environments. The MMC-DNN is inspired by the mechanism of the human eyes' lateral positioning, which consists of three modules called siamesed fully convolutional network (S-FCN), skip-connection fully convolutional autoencoder (SC-FCAE), and multitask neural network regressor (MT-NNR), respectively. The S-FCN is first designed to accurately detect the road area. Then, the segmented road was executed inverse perspective mapping and the result is fed to the developed SC-FCAE for extracting equivalent positioning features. Furthermore, the MT-NNR is proposed to efficiently estimate lateral position and yaw angle with the help of a road map. Based on the estimation results, the MEMS INS/GPS integration is significantly augmented by extended Kalman filter. Experimental results validate the effectiveness of the proposed framework in enhancing positioning performance.