Localization of robots is vital for navigation and path planning, such as in cases where a map of the environment is needed. Ultra-Wideband (UWB) for indoor location systems has been gaining popularity over the years with the introduction of low-cost UWB modules providing centimetre-level accuracy. However, in the presence of obstacles in the environment, Non-Line-Of-Sight (NLOS) measurements from the UWB will produce inaccurate results. As low-cost UWB devices do not provide channel information, we propose an approach to decide if a measurement is within Line-Of-Sight (LOS) or not by using some signal strength information provided by low-cost UWB modules through a Neural Network (NN) model. The result of this model is the probability of a ranging measurement being LOS which was used for localization through the Weighted-Least-Square (WLS) method. Our approach improves localization accuracy by 16.93% on the lobby testing data and 27.97% on the corridor testing data using the NN model trained with all extracted inputs from the office training data.
@article{arxiv.2206.09607,
title = {NLOS Ranging Mitigation with Neural Network Model for UWB Localization},
author = {Muhammad Shalihan and Ran Liu and Chau Yuen},
journal= {arXiv preprint arXiv:2206.09607},
year = {2022}
}
Comments
Accepted by 2022 IEEE International Conference on Automation Science and Engineering (CASE)