# Indoor WiFi-Beacon Dataset Construction Using Autonomous Low-Cost Robot for 3D Location Estimation
## 1 Introduction  
The indoor localization process has increased in popularity and received significant attention from both academia and industry. However, even with the huge existing number of studies on this topic, localization is still considered a big challenge and there is no optimal solution for all existing applications. Nowadays, various indoor networks, such as WiFi, Bluetooth, Zigbee, and more, have been used to obtain received-signal-strength (RSS) values to estimate an object's position, using it as the primary feature. Unlike other wireless technologies, the WiFi-fingerprint-localization system [1] does not require the deployment of extra infrastructure, as WiFi routers are already widely distributed in most indoor environments, such as residential buildings, commercial shops, airports... etc. Therefore, WiFi-based IPS has become one of the most promising systems these days. The WiFi-fingerprinting method is composed of two main phases: the training phase, for fingerprint-radio-map generation, and the estimation phase, for the user-location estimation process. The radio-map generation process is time-consuming and expensive, which is considered a drawback of this method. Therefore, the traditional systems for the radio-map creation process, which are based on manual collection such that the surveyorneeds to walk through the whole localization area to collect the WiFi fingerprints, are labor-intensive and time-consuming systems, especially in large-scale areas. As a result, a contemporary solution for the WiFi-fingerprinting training phase is the automated method (robot-based method) [2; 3], where a robot collects RSS and builds a radio map to be able to perform the location-estimation process. Indoor robot-based solutions have many potential applications in daily life, such as home care, data collection, object finding, and emergency support [4; 5]. On the other hand, designing autonomous robots is considered a critical issue since it requires the continuous computation of various characteristics of the robot, such as width, height, position of the sensors mounted on the robot, or wheel diameter, which can be used to make assumptions about the movement of the robot [6]. A robot developed by researchers in [7] is capable of generating a map using LiDAR sensors and simultaneous localization and mapping (SLAM) methods. The robot was equipped with an RGBD camera to improve the efficiency of the WiFi radio map, especially in featureless areas such as corridors. Despite this, the developed robot scanned 3000 points in 2 h without providing information about the experimental environment and the mean error of the positioning system. A robot's power needs become crucial when operating in large indoor environments. Researchers used a robot equipped with LiDAR and SLAM to construct a dual WiFi radio map (2.4 and 5.0 GHz) in [8] and, using a Bayesian probabilistic model and K-nearest-neighbor algorithm, they were able to reach a mean error of 2.4 m. The robot in this experiment consumed between 60 and 82 Wh and was able to scan two university floors in 94 min, making it an extremely power-efficient device. 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-collection-and-analysis (SDCA) technique. The WRM construction was improved by 12.9% with this method. Furthermore, they developed a robust tracking algorithm that can handle fluctuations in WiFi signals after collecting the data called the extended-Viterbi-algorithm and signal-fluctuation-matrix fusion tracking method (EVSFM). However, the author used a small environment with too many access points to achieve high-quality results, where the mean positioning error was about 1 m. In most indoor environments, this is not possible. In large indoor environments, RSS radio maps can be time-consuming to produce.