VNLSTM-PoseNet: A novel deep ConvNet for real-time 6-DOF camera relocalization in urban streets

Geo-spatial Information Science

Xuan Liao¹  Jiangying Qin²  Deren Li²  Bingxuan Guo²  Ruizhi Chen²  Ming Li³ 

[1] Department of Land Surveying and Geo-informatics, The Hong Kong Polytechnic University, Hong Kong, Chin;State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing, Wuhan University, Wuhan, Chin;State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing, Wuhan University, Wuhan, Chin;Department of Physics, ETH Zurich, Zurich, Switzerlan;

关键词: Camera relocalization;    pose regression;    deep convnet;    RGB image;    camera pose;   

DOI  :  10.1080/10095020.2021.1960779

来源: Taylor & Francis

【 摘 要 】

Image-based relocalization is a renewed interest in outdoor environments, because it is an important problem with many applications. PoseNet introduces Convolutional Neural Network (CNN) for the first time to realize the real-time camera pose solution based on a single image. In order to solve the problem of precision and robustness of PoseNet and its improved algorithms in complex environment, this paper proposes and implements a new visual relocation method based on deep convolutional neural networks (VNLSTM-PoseNet). Firstly, this method directly resizes the input image without cropping to increase the receptive field of the training image. Then, the image and the corresponding pose labels are put into the improved Long Short-Term Memory based (LSTM-based) PoseNet network for training and the network is optimized by the Nadam optimizer. Finally, the trained network is used for image localization to obtain the camera pose. Experimental results on outdoor public datasets show our VNLSTM-PoseNet can lead to drastic improvements in relocalization performance compared to existing state-of-the-art CNN-based methods.

【 授权许可】

CC BY