Robot Indoor Positioning and Navigation Based on Improved WiFi Location Fingerprint Positioning Algorithm

Abstract

In response to the traditional WiFi location fingerprint positioning algorithm still having a low positioning accuracy, which is difficult to meet the robot indoor positioning and navigation needs, a series of improvements are made to the traditional WiFi location fingerprint positioning algorithm, so that the positioning accuracy of the algorithm can be effectively improved. At the stage of building the location fingerprint library offline, WiFi signals are collected at each reference point by reducing the reference point spacing of the traditional location fingerprinting algorithm and then using different time period collection methods. The WiFi signal strength values are standardized using the standardization processing method to improve the specificity of traditional location fingerprinting. In the real-time localization stage, the WiFi signals collected from the unknown location points are averaged, and then, the fingerprint similarity calculation is performed using a matching method based on the magnitude of the Marxian distance as a similarity reference. In order to eliminate the location fingerprints that degrade the localization accuracy, an improved adaptive K -value WKNN algorithm is integrated at the end of the localization algorithm. The improved localization algorithm and the proposed raster-based navigation algorithm are validated in a fixed experimental environment. The experimental results show that the probability of the improved algorithm’s positioning error within 0.4 m is 49%, which is a 35% improvement over the conventional algorithm. Combining the improved positioning algorithm with our proposed grid-based navigation algorithm, the final navigation error probability within 0.8 m is 62%.

A Review of Sensing Technologies for Indoor Autonomous Mobile Robots

“…For WiFi-based positioning methods, RSSI is most commonly used because of its high accuracy. Hemin Ye et al [ 108 ] optimized the traditional WiFi fingerprint positioning method. Compared to traditional methods for fingerprint localization, this approach reduces the distance between sampling points and improves fingerprint matching accuracy by collecting and normalizing WiFi signals from different time periods.…”

A Development of an IoT-Based Connected University System: Progress Report

“…The challenges related to indoor positioning using Wi-Fi signals were summarized in our previous work [ 31 , 32 ]. An improved Wi-Fi location fingerprint positioning algorithm for robot indoor positioning and navigation was described in [ 33 ]. In order to eliminate the location fingerprints that degrade the localization accuracy, Ye and Peng integrated an improved adaptive K-value WKNN algorithm at the end of the localization algorithm.…”