A Prediction Method of Localizability Based on Deep Learning

• Published in: IEEE access Vol. 8; pp. 110103 - 110115
• Main Authors: Gao, Yang, Wang, Shu Qi, Li, Jing Hang, Hu, Meng Qi, Xia, Hong Yao, Hu, Hui, Wang, Lai Jun
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial neural networks; Covariance; Dead reckoning; Deep learning; Entropy; Environmental impact; Flow charts; Flow mapping; Laser beams; Localizability; map matching; Matching; mobile robot; Multilayers; neural network; Neural networks; Robot kinematics; Robot sensing systems; Uncertainty
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.3001177

As a basis of many missions, the accuracy of localization is highly important for mobile robots. For the generally used map matching based localization algorithms, the accuracy of localization, which is described by localizability, is greatly impacted by the environment. Consequently, this paper proposed a novel method to predict the localizability for the map matching based localization algorithms, based on the environment map. Firstly, the uncertainty of localization in map matching and dead-reckoning is analyzed based on which entropy of localization is chosen to describe the localizability instead of the generally used covariance. Next, based upon the flow chart of the map-based localization algorithm, a localizability predictor, which is composed of three different models, is designed to predict the entropy. Here a Convolutional Neural Network (CNN) is designed for the first model to predict the entropy of localization that comes from map matching. A Long Short-Term Memory (LSTM) neural network is designed for the second model to predict the entropy that comes from the dead-reckoning. Finally, a Multilayer fully connected Neural Network (MNN) is designed for the last model to predict the entropy after fusing the entropy results that come from the two models described above. Both simulation results and experimental results have proven that the proposed predictor can offer a better estimator of localizability compared to other existing approaches.