Global Positioning System Sensors Research Articles

Biosensor-Driven IoT Wearables for Accurate Body Motion Tracking and Localization.

The domain of human locomotion identification through smartphone sensors is witnessing rapid expansion within the realm of research. This domain boasts significant potential across various sectors, including healthcare, sports, security systems, home automation, and real-time location tracking. Despite the considerable volume of existing research, the greater portion of it has primarily concentrated on locomotion activities. Comparatively less emphasis has been placed on the recognition of human localization patterns. In the current study, we introduce a system by facilitating the recognition of both human physical and location-based patterns. This system utilizes the capabilities of smartphone sensors to achieve its objectives. Our goal is to develop a system that can accurately identify different human physical and localization activities, such as walking, running, jumping, indoor, and outdoor activities. To achieve this, we perform preprocessing on the raw sensor data using a Butterworth filter for inertial sensors and a Median Filter for Global Positioning System (GPS) and then applying Hamming windowing techniques to segment the filtered data. We then extract features from the raw inertial and GPS sensors and select relevant features using the variance threshold feature selection method. The extrasensory dataset exhibits an imbalanced number of samples for certain activities. To address this issue, the permutation-based data augmentation technique is employed. The augmented features are optimized using the Yeo-Johnson power transformation algorithm before being sent to a multi-layer perceptron for classification. We evaluate our system using the K-fold cross-validation technique. The datasets used in this study are the Extrasensory and Sussex Huawei Locomotion (SHL), which contain both physical and localization activities. Our experiments demonstrate that our system achieves high accuracy with 96% and 94% over Extrasensory and SHL in physical activities and 94% and 91% over Extrasensory and SHL in the location-based activities, outperforming previous state-of-the-art methods in recognizing both types of activities.

Indoor localization using device sensors: A threat to privacy

The localization techniques used in today’s smartphone are mainly based on Global Positioning System (GPS). However, GPS Sensors cannot work properly under in-door and underground locations. Therefore, many applications utilize device sensors such as accelerometer, gyrometer, and magnetometer for indoor localization. In this paper, we present a misuse case of how device sensors can be used to exploit the privacy of a user by geo-tracking. We propose an attack model through which the user location can be compromised without using the GPS sensors. The proposed attack model comprises of two stages. The first stage consists of deployment of the malicious application on the users’ smart-phones and gathering the information of various sensors in the background. The collected sensor data is uploaded to the malicious cloud server set up by the adversary. The second stage consists of pre-processing the sensor data received from the malicious cloud server and plot the user’s trajectory onto a graph in real-time. The proposed attack model is evaluated by developing two applications. The victim application tracks location, direction, and trajectory of the user without any location permission from the user. The proposed model achieves an accuracy of 98% without using special infrastructure and separate training phase. Further, we have discussed three mitigation schemes, which can be adapted by the Android developers in order to protect the user’s privacy.

Drowsiness and Crash Detection Mobile Application for Vehicle’s Safety

Detecting road accidents promptly is crucial for minimizing casualties and property damage worldwide. The proposed system, comprising hardware and a mobile application, automatically identifies and reports accidents to emergency services. It also employs a facial recognition system to detect driver drowsiness, enhancing accident prevention measures. By leveraging sensor technologies, cellular networks, and advanced detection algorithms, the proposed system analyzes data from accelerometers, Global System for Mobile Communication (GSM), and Global Positioning System (GPS) sensors. Originally designed for vehicles, it can be easily adapted for deployment in various settings such as factories and construction sites with minor adjustments. The system continuously monitors the driver's facial expressions and activities using sensors. When drowsiness is detected, it activates a buzzer, and in the event of a crash, it alerts the driver to prevent false alarms while simultaneously notifying the rescue center if a genuine crash has occurred. This integrated approach enhances safety and optimizes emergency response efforts. The Arduino microcontroller, equipped with an accelerometer, identifies sudden changes in motion like acceleration and rotation to assess impacts against predefined thresholds. Furthermore, GPS functionality accurately determines the vehicle's location at the time of the accident, while GSM enables seamless communication with rescue centers through notifications.

Multi-User System for Earth Sensing Spacecraft Attitude Calibration and Analysis

The Teledyne Multiple User System for Earth Sensing (“MUSES”) was developed for commercial Earth imaging and instrument technology development on the International Space Station (ISS). MUSES simultaneously hosts up to four Earth ensing instruments and provides inertial pointing and tracking to orient instruments toward planned targets on the ground. Up to four instruments are robotically installed on MUSES and periodically interchanged as warranted. MUSES was delivered to the ISS in June 2017, with initial testing successfully completed and subsequently documented in an initial requirements verification report. Assessing the performance of the MUSES attitude estimation system is challenging due to the lack of an independent attitude estimate of the platform attitude. A novel approach is introduced herein where the ISS attitude estimate, derived from ISS Global Positioning System sensors, is employed as an independent measure of MUSES platform attitude. Spectral decomposition methods are used to account for the contributions of ISS deformations to the variance in disparities between the ISS and MUSES attitudes and thus enable a less conservative estimate of the variance in MUSES attitude error. Implementing the spectral decomposition results in a best estimate upper bound on MUSES attitude errors of 34.5 and 27.7 arcsec, respectively, in the along-track and cross-track axes. Both the simulated and real results obtained from the spacecraft are presented.

Autonomous open-source electric wheelchair platform with internet-of-things and proportional-integral-derivative control

<span lang="EN-US">This study aims to improve the working model of autonomous wheelchair navigation for disabled patients using the internet of things (IoT). A proportional-integral-derivative (PID) control algorithm is applied to the autonomous wheelchair to control movement based on position coordinates and orientation provided by the global positioning system (GPS) and digital compass sensor. This system is controlled through the IoT system, which can be operated from a web browser. Autonomous wheelchairs are handled using a waypoint algorithm; ESP8266 is used as a microcontroller unit that acts as a bridge for transmitting data obtained by sensors and controlling the direct current (DC) motors as actuators. The proposed system and the autonomous wheelchair performance gave satisfactory results with a longitude and latitude error of 1.1 meters to 4.5 meters. This error is obtained because of the limitations of GPS with the type of Ublox Neo-M8N. As a starting point for further research, a mathematical model of a wheelchair was created, and pure pursuit control algorithm was used to simulate the movement. An open-source autonomous IoT platform for electric wheelchairs has been successfully created; this platform can help nurses and caretakers.</span>

An innovative medical waste management system in a smart city using XAI and vehicle routing optimization

Background The management of medical waste is a complex task that necessitates effective strategies to mitigate health risks, comply with regulations, and minimize environmental impact. In this study, a novel approach based on collaboration and technological advancements is proposed. Methods By utilizing colored bags with identification tags, smart containers with sensors, object recognition sensors, air and soil control sensors, vehicles with Global Positioning System (GPS) and temperature humidity sensors, and outsourced waste treatment, the system optimizes waste sorting, storage, and treatment operations. Additionally, the incorporation of explainable artificial intelligence (XAI) technology, leveraging scikit-learn, xgboost, catboost, lightgbm, and skorch, provides real-time insights and data analytics, facilitating informed decision-making and process optimization. Results The integration of these cutting-edge technologies forms the foundation of an efficient and intelligent medical waste management system. Furthermore, the article highlights the use of genetic algorithms (GA) to solve vehicle routing models, optimizing waste collection routes and minimizing transportation time to treatment centers. Conclusions Overall, the combination of advanced technologies, optimization algorithms, and XAI contributes to improved waste management practices, ultimately benefiting both public health and the environment.

DESIGN OF WIRELESS CONTROL SYSTEMS AND NAVIGATION SYSTEMS ON THE AUTONOMOUS VEHICLES AT HEAVY EQUIPMENT COMPANY

In this paper, the research development of the Autonomous Vehicle Prototype project has been carried out. The research was carried out on a golf cart (buggy) by designing wireless control and navigation systems. Designing a wireless control system on a golf cart aims to control the drive systems, steer systems, and brake systems. Furthermore, this system can be controlled electrically wirelessly through a Joystick Radio Controller, so that the golf cart can move forward, reverse, turn right and turn left. The navigation system aims to enable golf carts to track from point A to point B by using several sensors, including the GPS (Global Positioning System) sensor. GPS is used for tracking position on the golf cart, IMU (Inertia Measurement Unit) as a measure of the golf cart heading degree, LIDAR (Light Distance and Ranging) is used to detect distance and avoid obstacles, and Hall Effect Sensor as a speed sensor. The integration of the sensor and actuator systems is carried out using an Arduino Mega 2560 type microcontroller. Next, the movement from point A to point B is carried out autonomously. The final result of this research is that the GPS sensor can detect a track distance of 68.8 m with a minimum accuracy of 3 m and a resolution of 1 m. The IMU sensor can detect headings of 90o with 2o accuracy and 1o resolution. The LIDAR sensor can detect obstacles at a distance of 0 m – 4 m, and the Hall Effect sensor can detect the RPM of the car at a speed of 5 km/h. Golf cart control can be done wirelessly and automatically on the navigation system from point A to point B by avoiding obstacles.

94 Characterizing Yearling Beef Steers Grazing on Smooth Bromegrass Pasture Using Global Positioning Technology

Abstract Extensive grazing systems often vary in pasture size and terrain, making it challenging for ranchers to accurately locate livestock through visual appraisal. Using satellite-based global positioning systems (GPS), accurate position data can be provided to track animal movements that could inform grazing management decisions. This study aimed to: 1) assess two modern GPS devices for functionality by calculating fix success rate, and 2) evaluate the relationship between average daily distance traveled and average daily weight gain (ADG) performance on yearling steers grazing smooth bromegrass pasture using the GPS devices. Yearling steers (n = 25) grazed a 10.8-ha smooth bromegrass pasture for 156 days from May to September 2021. The temperature ranged from 4.1 to 38.8ºC with an average 22.7ºC and received 490 mm of rainfall. Cattle ADG was 0.65 ± 0.13 kg/d. Each steer was assigned two GPS collars for 28 days to compare two types of sensors. The sensors evaluated were i-gotU GT-600 travel loggers (GU) and Yabby LoRaWAN GPS sensors (YB) utilizing the IoT (Internet of Things) technology. The YB sensor possessed real-time monitoring and remote data access, while data from the GU sensor needed to be removed from the animals and manually downloaded. The YB sensors had a manufacturer declared finest positioning accuracy of 4.6 m, while that of the GU sensor being 9.1 m. With a 10-min sampling rate, battery life of the YB sensors lasted 4 weeks, while the GU sensors demonstrated unreliable battery performance that ranged between 1 to 4 weeks. Fixed success rate (FSR) was calculated for each sensor. Using PROC MIXED of SAS (v 9.4), on average the GU sensors had a 67.1% FSR and the YB sensors observed an 89.9% FSR (P < 0.0001). The YB sensor outweighed the GU sensor on battery life and FSR, thus was utilized throughout the duration of the grazing season. Individual animal traveled distance was calculated after converting the GPS coordinates in decimal degrees to Universal Transverse Mercator coordinates (WGS 1984 UTM zone 14N) in meters. The Euclidean distance between consecutive coordinates was calculated and summed daily to represent the daily traveled distance of an animal. Using all coordinates recorded from YB, steers traveled 2,718 ± 809 m/d. Statistical analysis was conducted using PROC CORR of SAS to determine the correlation between the daily averages of distance traveled and ADG. All values were considered significant α ≤ 0.05. There was no correlation observed between distance traveled and ADG (P ≥ 0.76). Results demonstrate that modern GPS sensors utilizing IoT technology have benefits over traditional GPS sensors and can be used as a tool for extensive grazing livestock tracking.

Analysis of In-Season External Load and Sport Performance in Women's Collegiate Basketball.

Brown, FSA, Fields, JB, Jagim, AR, Baker, RE, and Jones, MT. Analysis of in-season external load and sport performance in women's collegiate basketball. J Strength Cond Res 38(2): 318-324, 2024-Quantifying and monitoring athlete workload throughout a competitive season is a means to manage player readiness. Therefore, the purpose of the current study was to quantify practice and game external loads and to assess the relationship between such loads and basketball-specific performance metrics across a women's collegiate basketball season. Thirteen National Collegiate Athletic Association Division I women basketball athletes (age 20.08 ± 1.55 years) wore Global Positioning Systems sensors equipped with triaxial accelerometers for 29 games and 66 practices during the 2019-20 season. A multivariate analysis of variance was used to assess differences in external load between high- and low-minute players and across quarters within games ( p < 0.05). Bivariate Pearson correlation coefficients were run to determine relationships between external loads and metrics of basketball performance. Findings indicated that high- and low-minute athletes experienced different loads during games and practices ( p < 0.001). External loads differed by quarter, such that player load (PL) was highest in Q4 ( p = 0.007), PL·min -1 was highest in Q1 and lowest in Q4 ( p < 0.001), and explosive ratio (i.e., ratio of PL and explosive efforts) was lowest in Q3 ( p = 0.45). Relationships existed between PL·min -1 and field goals ( r = 0.41; p = 0.02) and between the explosive ratio and free throws ( r = 0.377 p = 0.04). These results can be used to inform design of training sessions with the intent to prepare athletes for the demands of the competitive season. It is recommended that future research continue to explore the relationship of sport-specific performance metrics and athlete external load.

GPS-Aided Automatic Site Survey Method for WiFi RTT-Based Positioning

The fine timing measurement protocol defined in the IEEE 802.11-2016 standard leads to precise ranging between WiFi devices. However, because the locations of WiFi access points (APs) are required for locating mobile devices, a site survey must be performed prior to the deployment of positioning services, which is extremely time-consuming. This paper studies an automated site survey method that utilize the global positioning system (GPS) and built-in sensors in mobile devices. When a user enters an indoor site from outside, the outside trajectory is obtained using GPS and built-in sensors, and the indoor trajectory can be estimated solely relying on the sensor measurements. Thereafter, the locations of APs can be determined using the ranging results obtained at multiple positions on the estimated indoor trajectory. The experimental results revealed that the difference in average positioning error obtained using the estimated and perfect AP locations was 30 cm different from the error using perfect AP locations

An innovative medical waste management system in a smart city using XAI and vehicle routing optimization.

The management of medical waste is a complex task that necessitates effective strategies to mitigate health risks, comply with regulations, and minimize environmental impact. In this study, a novel approach based on collaboration and technological advancements is proposed. By utilizing colored bags with identification tags, smart containers with sensors, object recognition sensors, air and soil control sensors, vehicles with Global Positioning System (GPS) and temperature humidity sensors, and outsourced waste treatment, the system optimizes waste sorting, storage, and treatment operations. Additionally, the incorporation of explainable artificial intelligence (XAI) technology, leveraging scikit-learn, xgboost, catboost, lightgbm, and skorch, provides real-time insights and data analytics, facilitating informed decision-making and process optimization. The integration of these cutting-edge technologies forms the foundation of an efficient and intelligent medical waste management system. Furthermore, the article highlights the use of genetic algorithms (GA) to solve vehicle routing models, optimizing waste collection routes and minimizing transportation time to treatment centers. Overall, the combination of advanced technologies, optimization algorithms, and XAI contributes to improved waste management practices, ultimately benefiting both public health and the environment.

An effective fusion positioning methodology for land vehicles in GPS-denied environments using low-cost sensors

It is still a main challenge to achieve accurate and reliable vehicle positioning in Global Position System (GPS)-denied environments using low-cost sensors. Although existing methods have obtained a certain performance improvement, there is still room for further enhancement in positioning accuracy due to the lack of effective observation information during GPS outages. To address this challenge, this paper proposes an effective fusion positioning methodology based on enhanced observation information. Firstly, a data denoising algorithm based on convolutional denoising autoencoder is developed to effectively filter the noises in inertial sensors, so as to provide more clean data for subsequent modeling. Then, a differential fusion strategy is designed to selectively fuse multiple observation information from monocular camera, low-cost GPS and wheel speed sensor with extended Kalman filter, which can further improve the positioning accuracy. Finally, a long short-term memory-based error prediction model is constructed to learn the mapping relationship between the result of data denoising and differential fusion. In the case of GPS outages, the error model provides accurate corrections to compensate for position errors of inertial navigation system. The performance of the proposed methodology was evaluated on real-world data collected in complex urban environments. Experimental results indicate that the proposed positioning methodology can obtain significant accuracy enhancement.

Vehicle positioning in urban environments using particle filtering-based global positioning system, odometry, and map data fusion

&lt;p&gt;This article presents a new method for land vehicle navigation using global positioning system (GPS), dead reckoning sensor (DR), and digital road map information, particularly in urban environments where GPS failures can occur. The odometer sensors and map measure can be used to provide continuous navigation and correct the vehicle location in the presence of GPS masking. To solve this estimation problem for vehicle navigation, we propose to use particle filtering for GPS/odometer/map integration. The particle filter is a method based on the Bayesian estimation technique and the Monte Carlo method, which deals with non-linear models and is not limited to Gaussian statistics. When the GPS sensor cannot provide a location due to the number of satellites in view, the filter fuses the limited GPS pseudo-range data to enhance the vehicle positioning. The developed filter is then tested in a transportation network scenario in the presence of GPS failures, which shows the advantages of the proposed approach for vehicle location compared to the extended Kalman filter.&lt;/p&gt;

TEC and ROTI Measurements from a New GPS Receiver at BOWEN University, Nigeria

Scintillation and total electron content (TEC) are the two major examples of the top-side ionospheric parameters that are recorded differently by most Global Positioning System (GPS) receivers. The new GPS sensor created by the Atmospheric and Space Technology Research Associates (ASTRA), Cornell University, and the University of Texas, Austin have capability to record scintillation and TEC fluctuations simultaneously. Hence, the Connected Autonomous Space Environment Sensor (CASES) from ASTRA is a software-defined GPS receiver with the dual frequency of L1 C/A and L2C codes for space-weather monitoring and can be remotely programmed via an internet source. The receiver employs numerous novel techniques that make it suitable for space-weather studies compared to other nearby GPS receivers, such as different methods for eliminating local clock effects, an advanced triggering mechanism for determining scintillation onset, data buffering to permit observation of the prelude to scintillation, and data-bit prediction and wipe-off for robust tracking. Moreover, the CASES hardware is made up of a custom-built dual frequency, a digital signal processor board, and a “single board computer” with an ARM microcontroller. We have used the CASES GPS receiver newly installed at Bowen University, Iwo, Nigeria, to investigate the TEC and the rate of the TEC index (ROTI) around the equatorial region. Measurements of the TEC and ROTI showed similar variation trends in monthly, seasonal, and annual periods when compared to TEC and ROTI measurements from a nearby station, BJCO at Cotonou, Benin Republic. The newly installed GPS receiver looks promising for scientific use as it is the only one operational in Nigeria at the moment.

Smartphone Global Positioning System-Based System to Assess Mobility in Health Research: Development, Accuracy, and Usability Study.

As global positioning system (GPS) measurement is getting more precise and affordable, health researchers can now objectively measure mobility using GPS sensors. Available systems, however, often lack data security and means of adaptation and often rely on a permanent internet connection. To overcome these issues, we aimed to develop and test an easy-to-use, easy-to-adapt, and offline working app using smartphone sensors (GPS and accelerometry) for the quantification of mobility parameters. An Android app, a server backend, and a specialized analysis pipeline have been developed (development substudy). Parameters of mobility by the study team members were extracted from the recorded GPS data using existing and newly developed algorithms. Test measurements were performed with participants to complete accuracy and reliability tests (accuracy substudy). Usability was examined by interviewing community-dwelling older adults after 1 week of device use, followed by an iterative app design process (usability substudy). The study protocol and the software toolchain worked reliably and accurately, even under suboptimal conditions, such as narrow streets and rural areas. The developed algorithms had high accuracy (97.4% correctness, F1-score=0.975) in distinguishing dwelling periods from moving intervals. The accuracy of the stop/trip classification is fundamental to second-order analyses such as the time out of home, as they rely on a precise discrimination between the 2 classes. The usability of the app and the study protocol was piloted with older adults, which showed low barriers and easy implementation into daily routines. Based on accuracy analyses and users' experience with the proposed system for GPS assessments, the developed algorithm showed great potential for app-based estimation of mobility in diverse health research contexts, including mobility patterns of community-dwelling older adults living in rural areas. RR2-10.1186/s12877-021-02739-0.

MEDEA randomised intervention study protocol in Cyprus, Greece and Israel for mitigation of desert dust health effects in adults with atrial fibrillation

IntroductionMediterranean countries experience frequent desert dust storm (DDS) events originating from neighbouring Sahara and Arabian deserts, which are associated with significant increase in mortality and hospital admissions, mostly from cardiovascular...

LocateUAV: Unmanned Aerial Vehicle Location Estimation via Contextual Analysis in an IoT Environment

Object detection supported by unmanned aerial vehicles (UAVs) has generated significant interest in recent years including applications, such as surveillance, search for missing persons, traffic, and disaster management. Location awareness is a challenging task, particularly, the deployment of UAVs in a global positioning system (GPS) restricted environment or GPS sensor failure. To mitigate this problem, we propose LocateUAV, a novel location awareness framework, to detect UAV’s location by processing the data from the visual sensor in real time using a lightweight convolutional neural network (CNN). Assuming that the drone is in an IoT environment, first, the object detection technique is applied to detect the object of interest (OOI), namely, signboard. Subsequently, optical character recognition (OCR) is applied to extract useful contextual information. In the final step, the extracted information is forwarded to the map application programming interface (API) to locate the UAV. We also present a newly created data set for LocateUAV, which comprises challenging scenarios for context analysis. Moreover, we also compress an existing lightweight model up to 45 MB for efficient processing over UAV, which is 19.5% when compared with the size of the original model. Finally, an in-depth comparison of various trained and efficient object detection and OCR techniques is presented to facilitate future research on the development of flex drone that can extract information from the surroundings of a location in a GPS-restricted environment.

Trajectory Planning for Multiple UAVs and Hierarchical Collision Avoidance Based on Nonlinear Kalman Filters

Fully autonomous trajectory planning for multiple unmanned aerial vehicles (UAVs) is significant for building the next generation of the logistics industry without human control. This paper presents a method to enable multiple UAVs to fly in the same trajectory without collision. It benefits several applications, such as smart cities and transfer goods, during the COVID-19 pandemic. Different types of nonlinear state estimation are deployed to test the position estimation of drones by treating the information from AirSim as offline dynamic data. The obtained global positioning system sensor data and magnetometer sensor data are determined as the measurement model. The experiment in the simulation is separated into (1) the localization state, (2) the rendezvous state, in which the proposed rendezvous strategy is presented by using the relation between velocity and displacement through the setting area, and (3) the full mission state, which combines both the localization and rendezvous states. The localization state results show the best RMSE in the case of full GPS available at 0.21477 m and 0.25842 m in the case of a GPS outage during a period of time by implementing the ensemble Kalman filter. Similarly, the ensemble Kalman filter performs well with an RMSE of 0.5112414 m in the rendezvous state and demonstrates exceptional performance in the full mission state. Moreover, the experiment is implemented in a real-world situation with some basic drone kits as proof that the proposed rendezvous strategy can truly operate.

Inferring Trip Destination Purposes for Trip Records Collected through Smartphone Apps

Rapid developments in smartphones and Global Positioning System (GPS) technology have provided a new method for travel data collection. People’s travel trajectories can be passively collected through smartphones with built-in GPS sensors and processed to infer important attributes of travel behavior such as trip destination purposes. This paper examines the feasibility of using discrete choice models and tree-based machine learning models for trip purpose inference. Using smartphone GPS trajectories and land-use data that are open-sourced for academic research, four models are developed to classify trip destination purposes into one of thirteen categories. The models include multinomial logit, mixed logit, random forest, and gradient boosting decision tree. Because the data set is significantly unbalanced with more than 50% of the trips being home or work trips, a two-stage modeling process is applied to identify home and work trips first and then subsequently to classify trips for the remaining discretionary purposes. The results show that both discrete choice models and machine learning models can achieve more than 70% prediction accuracies for home trips and work trips, but less than 30% prediction accuracies for most of the discretionary trip purposes. The discrete choice models can accurately reproduce the market share distribution of the trip purposes, but the machine learning models fail to do so. The results imply that as purely data-driven models, machine learning models may not be the best solution for trip purpose inference because they lack the theoretical background provided by microeconomics and human psychology that are essential in explaining people’s travel and activity choices.

OL-SLAM: A Robust and Versatile System of Object Localization and SLAM

This paper proposes a real-time, versatile Simultaneous Localization and Mapping (SLAM) and object localization system, which fuses measurements from LiDAR, camera, Inertial Measurement Unit (IMU), and Global Positioning System (GPS). Our system can locate itself in an unknown environment and build a scene map based on which we can also track and obtain the global location of objects of interest. Precisely, our SLAM subsystem consists of the following four parts: LiDAR-inertial odometry, Visual-inertial odometry, GPS-inertial odometry, and global pose graph optimization. The target-tracking and positioning subsystem is developed based on YOLOv4. Benefiting from the use of GPS sensor in the SLAM system, we can obtain the global positioning information of the target; therefore, it can be highly useful in military operations, rescue and disaster relief, and other scenarios.