Fusion of the SLAM with Wi-Fi-Based Positioning Methods for Mobile Robot-Based Learning Data Collection, Localization, and Tracking in Indoor Spaces

Abstract

The ability to estimate the current locations of mobile robots that move in a limited workspace and perform tasks is fundamental in robotic services. However, even if the robot is given a map of the workspace, it is not easy to quickly and accurately determine its own location by relying only on dead reckoning. In this paper, a new signal fluctuation matrix and a tracking algorithm that combines the extended Viterbi algorithm and odometer information are proposed to improve the accuracy of robot location tracking. In addition, to collect high-quality learning data, we introduce a fusion method called simultaneous localization and mapping and Wi-Fi fingerprinting techniques. The results of the experiments conducted in an office environment confirm that the proposed methods provide accurate and efficient tracking results. We hope that the proposed methods will also be applied to different fields, such as the Internet of Things, to support real-life activities.

A Hybrid Marker-Based Indoor Positioning System for Pedestrian Tracking in Subway Stations

“…The proposed HMIPS uses the method devised in our previous work, extended Viterbi tracking using the hidden Markov model (HMM) [38] and a study [39] which corrects errors when using PDR. An HMM modeling for marker-based indoor localization, the hidden states HS =< hs 1 , hs 2 , .…”

“…Because a pedestrian can only walk at one interval in a limited way, they cannot move too far from the previous state or move through walls or any other barriers. As discussed in [38,40], gyroscope data are used for estimating the user moving direction, and an accelerometer is used for detecting the user's steps. The current state s can be calculated using the emission probability B at t − 1, the conditional probability of the moving direction, and the moving distance.…”

A review of visual SLAM for robotics: evolution, properties, and future applications

“…Furthermore, it provides real-time capabilities, allowing robots to make decisions on-the-fly without relying on pre-existing maps. Its utility extends to the extraction, organization, and comprehension of information, thereby enhancing the robot’s capacity to interpret and interact effectively with its environment ( Pal et al, 2022 ; Lee et al, 2020 ; Aslan et al, 2021 ). It is crucial to enable these robots to autonomously navigate and interact in human environments, thus reducing human effort and enhancing overall productivity ( Arfa, 2022 ).…”

Indoor WiFi-Beacon Dataset Construction Using Autonomous Low-Cost Robot for 3D Location Estimation

“…According to [9], a robot-based method was adopted to collect data more effectively and quickly. By analyzing the map's reliability and collecting additional learning data, a reliable WiFi radio map (WRM) was constructed using the SLAM-based data-collectionand-analysis (SDCA) technique.…”