English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

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.

A Prediction Method of Localizability Based on Deep Learning