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Navigation and Perception Sensors and Systems for Robots, Autonomous Vehicles, and Wearable Technology

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Navigation and Positioning".

Deadline for manuscript submissions: closed (10 August 2023) | Viewed by 54286

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Special Issue Editors

Assoc. Prof. Umar Iqbal

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Department of Electrical Engineering, Illinois State University, Normal, IL 61761, USA
Interests: multi-sensor data fusion and integrated positioning technologies; navigation systems and control systems; robotics and autonomous platforms; optimization techniques; adaptive filtering; signal processing; multi-resolution data analysis; IoT; wearable technology; UAV applications; biomechanics; human-machine interaction; cybersecurity for autonomous systems; educational technologies; design thinking; and AI-powered solutions for applications in aviation, agriculture, healthcare, and intelligent systems
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Prof. Dr. Michael J. Korenberg

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Department of Electrical and Computer Engineering, Queen's University, Kingston, ON K7L 3N6, Canada
Interests: signal processing; sensor data fusion; applied optimal estimation; artificial intelligence; nonlinear system identification; fast orthogonal search; parallel cascade identification
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Sensors are an indispensable part of perceiving the environments for robots and autonomous vehicles. With advances in wearable sensor devices and technology, the domain is extended to humans. A variety of wearable sensors are utilized in sports, medicine, military, entertainment, personalized safety for monitoring, navigation, perception, and surveillance.

A suite of multiple sensors with complementary modalities with distinct strengths and weaknesses is required to achieve overall awareness of the different environments for large- and small-scale decision-making. The sensors used for navigation and perception of robots, autonomous vehicles, and wearable technology include, but are not limited to, accelerometers, gyroscopes, magnetometers, cameras, radar, ultrasound, LiDAR, etc. Moreover, augmentation of these sensors with GNSS, Wi-Fi, LTE, Bluetooth, ultrasonic, sonar, and A-GPS further enhances an integrated system's performance.

This Special Issue's main objective is to feature navigation and perception applications related to robots, autonomous vehicles, and wearable technology. We invite innovative and original research contributions to cover all aspects of the development and application of the following and related topics, including but not limited to:

Sensor Architecture and Calibration
Signal Processing
Sensor Data Fusion
Applied Optimal Estimation
Multi-Sensor Integration
Sensing, Monitoring, and Mapping
Remote Monitoring
Laser Scanning
Computer Vision
Indoor Navigation and Perception
Navigation and Visual Scanning Technologies
Vehicle and Personal Location
Artificial Intelligence
Decision Support
Innovative Applications of Sensors for Navigation and Perception of Robots, Autonomous Vehicles, and Wearable Technology

Assoc. Prof. Umar Iqbal
Prof. Dr. Michael J. Korenberg
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

sensor data fusion
outdoor/indoor navigation and perception
robots
autonomous vehicles
wearable technology
intelligent sensors
monitoring
mapping

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Published Papers (14 papers)

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18 pages, 5580 KiB

Open AccessArticle

Why You Cannot Rank First: Modifications for Benchmarking Six-Degree-of-Freedom Visual Localization Algorithms

by Sheng Han, Wei Gao and Zhanyi Hu

Sensors 2023, 23(23), 9580; https://doi.org/10.3390/s23239580 - 2 Dec 2023

Viewed by 1216

Abstract

Robust and precise visual localization over extended periods of time poses a formidable challenge in the current domain of spatial vision. The primary difficulty lies in effectively addressing significant variations in appearance caused by seasonal changes (summer, winter, spring, autumn) and diverse lighting conditions (dawn, day, sunset, night). With the rapid development of related technologies, more and more relevant datasets have emerged, which has also promoted the progress of 6-DOF visual localization in both directions of autonomous vehicles and handheld devices.This manuscript endeavors to rectify the existing limitations of the current public benchmark for long-term visual localization, especially in the part on the autonomous vehicle challenge. Taking into account that autonomous vehicle datasets are primarily captured by multi-camera rigs with fixed extrinsic camera calibration and consist of serialized image sequences, we present several proposed modifications designed to enhance the rationality and comprehensiveness of the evaluation algorithm. We advocate for standardized preprocessing procedures to minimize the possibility of human intervention influencing evaluation results. These procedures involve aligning the positions of multiple cameras on the vehicle with a predetermined canonical reference system, replacing the individual camera positions with uniform vehicle poses, and incorporating sequence information to compensate for any failed localized poses. These steps are crucial in ensuring a just and accurate evaluation of algorithmic performance. Lastly, we introduce a novel indicator to resolve potential ties in the Schulze ranking among submitted methods. The inadequacies highlighted in this study are substantiated through simulations and actual experiments, which unequivocally demonstrate the necessity and effectiveness of our proposed amendments. Full article

(This article belongs to the Special Issue Navigation and Perception Sensors and Systems for Robots, Autonomous Vehicles, and Wearable Technology)

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16 pages, 1764 KiB

Open AccessArticle

Performance Analysis of Relative GPS Positioning for Low-Cost Receiver-Equipped Agricultural Rovers

by Gustavo S. Carvalho, Felipe O. Silva, Marcus Vinicius O. Pacheco and Gleydson A. O. Campos

Sensors 2023, 23(21), 8835; https://doi.org/10.3390/s23218835 - 30 Oct 2023

Cited by 6 | Viewed by 1813

Abstract

Global navigation satellite systems (GNSSs) became an integral part of all aspects of our lives, whether for positioning, navigation, or timing services. These systems are central to a range of applications including road, aviation, maritime, and location-based services, agriculture, and surveying. The Global Positioning System (GPS) Standard Position Service (SPS) provides position accuracy up to 10 m. However, some modern-day applications, such as precision agriculture (PA), smart farms, and Agriculture 4.0, have demanded navigation technologies able to provide more accurate positioning at a low cost, especially for vehicle guidance and variable rate technology purposes. The Society of Automotive Engineers (SAE), for instance, through its standard J2945 defines a maximum of 1.5 m of horizontal positioning error at 68% probability (

1 σ

), aiming at terrestrial vehicle-to-vehicle (V2V) applications. GPS position accuracy may be improved by addressing the common-mode errors contained in its observables, and relative GNSS (RGNSS) is a well-known technique for overcoming this issue. This paper builds upon previous research conducted by the authors and investigates the sensitivity of the position estimation accuracy of low-cost receiver-equipped agricultural rovers as a function of two degradation factors that RGNSS is susceptible to: communication failures and baseline distances between GPS receivers. The extended Kalman filter (EKF) approach is used for position estimation, based on which we show that it is possible to achieve 1.5 m horizontal accuracy at 68% probability (

1 σ

) for communication failures up to 3000 s and baseline separation of around 1500 km. Experimental data from the Brazilian Network for Continuous Monitoring of GNSS (RBMC) and a moving agricultural rover equipped with a low-cost GPS receiver are used to validate the analysis. Full article

(This article belongs to the Special Issue Navigation and Perception Sensors and Systems for Robots, Autonomous Vehicles, and Wearable Technology)

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16 pages, 12602 KiB

Open AccessArticle

Conditional Generative Models for Dynamic Trajectory Generation and Urban Driving

by David Paz, Hengyuan Zhang, Hao Xiang, Andrew Liang and Henrik I. Christensen

Sensors 2023, 23(15), 6764; https://doi.org/10.3390/s23156764 - 28 Jul 2023

Cited by 1 | Viewed by 2347

Abstract

This work explores methodologies for dynamic trajectory generation for urban driving environments by utilizing coarse global plan representations. In contrast to state-of-the-art architectures for autonomous driving that often leverage lane-level high-definition (HD) maps, we focus on minimizing required map priors that are needed to navigate in dynamic environments that may change over time. To incorporate high-level instructions (i.e., turn right vs. turn left at intersections), we compare various representations provided by lightweight and open-source OpenStreetMaps (OSM) and formulate a conditional generative model strategy to explicitly capture the multimodal characteristics of urban driving. To evaluate the performance of the models introduced, a data collection phase is performed using multiple full-scale vehicles with ground truth labels. Our results show potential use cases in dynamic urban driving scenarios with real-time constraints. The dataset is released publicly as part of this work in combination with code and benchmarks. Full article

(This article belongs to the Special Issue Navigation and Perception Sensors and Systems for Robots, Autonomous Vehicles, and Wearable Technology)

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19 pages, 5409 KiB

Open AccessArticle

Sensor-Based Classification of Primary and Secondary Car Driver Activities Using Convolutional Neural Networks

by Rafał Doniec, Justyna Konior, Szymon Sieciński, Artur Piet, Muhammad Tausif Irshad, Natalia Piaseczna, Md Abid Hasan, Frédéric Li, Muhammad Adeel Nisar and Marcin Grzegorzek

Sensors 2023, 23(12), 5551; https://doi.org/10.3390/s23125551 - 13 Jun 2023

Cited by 6 | Viewed by 2294

Abstract

To drive safely, the driver must be aware of the surroundings, pay attention to the road traffic, and be ready to adapt to new circumstances. Most studies on driving safety focus on detecting anomalies in driver behavior and monitoring cognitive capabilities in drivers. In our study, we proposed a classifier for basic activities in driving a car, based on a similar approach that could be applied to the recognition of basic activities in daily life, that is, using electrooculographic (EOG) signals and a one-dimensional convolutional neural network (1D CNN). Our classifier achieved an accuracy of 80% for the 16 primary and secondary activities. The accuracy related to activities in driving, including crossroad, parking, roundabout, and secondary activities, was 97.9%, 96.8%, 97.4%, and 99.5%, respectively. The F1 score for secondary driving actions (0.99) was higher than for primary driving activities (0.93–0.94). Furthermore, using the same algorithm, it was possible to distinguish four activities related to activities of daily life that were secondary activities when driving a car. Full article

(This article belongs to the Special Issue Navigation and Perception Sensors and Systems for Robots, Autonomous Vehicles, and Wearable Technology)

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18 pages, 6509 KiB

Open AccessArticle

Deep Deterministic Policy Gradient-Based Autonomous Driving for Mobile Robots in Sparse Reward Environments

by Minjae Park, Seok Young Lee, Jin Seok Hong and Nam Kyu Kwon

Sensors 2022, 22(24), 9574; https://doi.org/10.3390/s22249574 - 7 Dec 2022

Cited by 12 | Viewed by 3377

Abstract

In this paper, we propose a deep deterministic policy gradient (DDPG)-based path-planning method for mobile robots by applying the hindsight experience replay (HER) technique to overcome the performance degradation resulting from sparse reward problems occurring in autonomous driving mobile robots. The mobile robot in our analysis was a robot operating system-based TurtleBot3, and the experimental environment was a virtual simulation based on Gazebo. A fully connected neural network was used as the DDPG network based on the actor–critic architecture. Noise was added to the actor network. The robot recognized an unknown environment by measuring distances using a laser sensor and determined the optimized policy to reach its destination. The HER technique improved the learning performance by generating three new episodes with normal experience from a failed episode. The proposed method demonstrated that the HER technique could help mitigate the sparse reward problem; this was further corroborated by the successful autonomous driving results obtained after applying the proposed method to two reward systems, as well as actual experimental results. Full article

(This article belongs to the Special Issue Navigation and Perception Sensors and Systems for Robots, Autonomous Vehicles, and Wearable Technology)

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25 pages, 31903 KiB

Open AccessArticle

Power Tower Inspection Simultaneous Localization and Mapping: A Monocular Semantic Positioning Approach for UAV Transmission Tower Inspection

by Zhiying Liu, Xiren Miao, Zhiqiang Xie, Hao Jiang and Jing Chen

Sensors 2022, 22(19), 7360; https://doi.org/10.3390/s22197360 - 28 Sep 2022

Cited by 5 | Viewed by 2843

Abstract

Realizing autonomous unmanned aerial vehicle (UAV) inspection is of great significance for power line maintenance. This paper introduces a scheme of using the structure of a tower to realize visual geographical positioning of UAV for tower inspection and presents a monocular semantic simultaneous localization and mapping (SLAM) framework termed PTI-SLAM (power tower inspection SLAM) to cope with the challenge of a tower inspection scene. The proposed scheme utilizes prior knowledge of tower component geolocation and regards geographical positioning as the estimation of transformation between SLAM and the geographic coordinates. To accomplish the robust positioning and semi-dense semantic mapping with limited computing power, PTI-SLAM combines the feature-based SLAM method with a fusion-based direct method and conveys a loosely coupled architecture of a semantic task and a SLAM task. The fusion-based direct method is specially designed to overcome the fragility of the direct method against adverse conditions concerning the inspection scene. Experiment results show that PTI-SLAM inherits the robustness advantage of the feature-based method and the semi-dense mapping ability of the direct method and achieves decimeter-level real-time positioning in the airborne system. The experiment concerning geographical positioning indicates more competitive accuracy compared to the previous visual approach and artificial UAV operating, demonstrating the potential of PTI-SLAM. Full article

(This article belongs to the Special Issue Navigation and Perception Sensors and Systems for Robots, Autonomous Vehicles, and Wearable Technology)

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19 pages, 7098 KiB

Open AccessArticle

Automatic Landing of Unmanned Aerial Vehicles via Wireless Positioning System with Pseudo-Conical Scanning

by Ilia Iliev and Ivaylo Nachev

Sensors 2022, 22(17), 6451; https://doi.org/10.3390/s22176451 - 26 Aug 2022

Cited by 5 | Viewed by 2312

Abstract

In this work, a wireless UAV unmanned landing system is considered, using the principles of pseudo conical scanning with a phased antenna array (PAA). The basic requirements for the characteristics and parameters of the system as a whole and of its components are defined. Special attention is paid to the primary sensor of the system—PAA with electronic scanning. A variant with a minimum number of four states on the radiation pattern of a low-budget patch PAA was studied. A linear regression of the difference characteristics of the measured radiation beams is proposed, which allows the practical application of the recommended landing algorithm with low computational complexity. Systematic and random positioning errors, both by measurement and by Monte Carlo simulation, were studied. Obtained statistical results prove the algorithm convergence and acceptable accuracy for the system implementation. They are applied if necessary to adjust the Kalman filter parameters. The proposed wireless system can be used for unmanned landing, tracking, and navigating the UAV in flight, or wireless navigation of other mobile objects. Full article

(This article belongs to the Special Issue Navigation and Perception Sensors and Systems for Robots, Autonomous Vehicles, and Wearable Technology)

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16 pages, 4092 KiB

Open AccessArticle

LiDAR-OSM-Based Vehicle Localization in GPS-Denied Environments by Using Constrained Particle Filter

by Mahdi Elhousni, Ziming Zhang and Xinming Huang

Sensors 2022, 22(14), 5206; https://doi.org/10.3390/s22145206 - 12 Jul 2022

Cited by 5 | Viewed by 8871

Abstract

Cross-modal vehicle localization is an important task for automated driving systems. This research proposes a novel approach based on LiDAR point clouds and OpenStreetMaps (OSM) via a constrained particle filter, which significantly improves the vehicle localization accuracy. The OSM modality provides not only a platform to generate simulated point cloud images, but also geometrical constraints (e.g., roads) to improve the particle filter’s final result. The proposed approach is deterministic without any learning component or need for labelled data. Evaluated by using the KITTI dataset, it achieves accurate vehicle pose tracking with a position error of less than 3 m when considering the mean error across all the sequences. This method shows state-of-the-art accuracy when compared with the existing methods based on OSM or satellite maps. Full article

(This article belongs to the Special Issue Navigation and Perception Sensors and Systems for Robots, Autonomous Vehicles, and Wearable Technology)

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23 pages, 7384 KiB

Open AccessArticle

A UWB-Based Lighter-Than-Air Indoor Robot for User-Centered Interactive Applications

by Khawar Naheem, Ahmed Elsharkawy, Dongwoo Koo, Yundong Lee and Munsang Kim

Sensors 2022, 22(6), 2093; https://doi.org/10.3390/s22062093 - 8 Mar 2022

Cited by 10 | Viewed by 3841

Abstract

Features such as safety and longer flight times render lighter-than-air robots strong candidates for indoor navigation applications involving people. However, the existing interactive mobility solutions using such robots lack the capability to follow a long-distance user in a relatively larger indoor space. At the same time, the tracking data delivered to these robots are sensitive to uncertainties in indoor environments such as varying intensities of light and electromagnetic field disturbances. Regarding the above shortcomings, we proposed an ultra-wideband (UWB)-based lighter-than-air indoor robot for user-centered interactive applications. We developed the data processing scheme over a robot operating system (ROS) framework to accommodate the robot’s integration needs for a user-centered interactive application. In order to explore the user interaction with the robot at a long-distance, the dual interactions (i.e., user footprint following and user intention recognition) were proposed by equipping the user with a hand-held UWB sensor. Finally, experiments were conducted inside a professional arena to validate the robot’s pose tracking in which 3D positioning was compared with the 3D laser sensor, and to reveal the applicability of the user-centered autonomous following of the robot according to the dual interactions. Full article

(This article belongs to the Special Issue Navigation and Perception Sensors and Systems for Robots, Autonomous Vehicles, and Wearable Technology)

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22 pages, 1209 KiB

Open AccessArticle

A GRASP-Based Approach for Planning UAV-Assisted Search and Rescue Missions

by Casper Bak Pedersen, Kasper Gaj Nielsen, Kasper Rosenkrands, Alex Elkjær Vasegaard, Peter Nielsen and Mohamed El Yafrani

Sensors 2022, 22(1), 275; https://doi.org/10.3390/s22010275 - 30 Dec 2021

Cited by 8 | Viewed by 2834

Abstract

Search and Rescue (SAR) missions aim to search and provide first aid to persons in distress or danger. Due to the urgency of these situations, it is important to possess a system able to take fast action and effectively and efficiently utilise the available resources to conduct the mission. In addition, the potential complexity of the search such as the ruggedness of terrain or large size of the search region should be considered. Such issues can be tackled by using Unmanned Aerial Vehicles (UAVs) equipped with optical sensors. This can ensure the efficiency in terms of speed, coverage and flexibility required to conduct this type of time-sensitive missions. This paper centres on designing a fast solution approach for planning UAV-assisted SAR missions. The challenge is to cover an area where targets (people in distress after a hurricane or earthquake, lost vessels in sea, missing persons in mountainous area, etc.) can be potentially found with a variable likelihood. The search area is modelled using a scoring map to support the choice of the search sub-areas, where the scores represent the likelihood of finding a target. The goal of this paper is to propose a heuristic approach to automate the search process using scarce heterogeneous resources in the most efficient manner. Full article

(This article belongs to the Special Issue Navigation and Perception Sensors and Systems for Robots, Autonomous Vehicles, and Wearable Technology)

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21 pages, 4800 KiB

Open AccessArticle

Monocular Visual Position and Attitude Estimation Method of a Drogue Based on Coaxial Constraints

by Kedong Zhao, Yongrong Sun, Yi Zhang and Hua Li

Sensors 2021, 21(16), 5673; https://doi.org/10.3390/s21165673 - 23 Aug 2021

Cited by 7 | Viewed by 2592

Abstract

In aerial refueling, there exists deformation of the circular feature on the drogue’s stabilizing umbrella to a certain extent, which causes the problem of duality of position estimation by a single circular feature. In this paper, a monocular visual position and attitude estimation method of a drogue is proposed based on the coaxial constraints. Firstly, a procedure for scene recovery from one single circle is introduced. The coaxial constraints of the drogue are proposed and proved to be useful for the duality’s elimination by analyzing the matrix of the spatial structure. Furthermore, we came up with our method, which is composed of fitting the parameters of the spatial circles by restoring the 3D points on it, using the two-level coaxial constraints to eliminate the duality, and optimizing the normal vector of the plane where the inner circle is located. Finally, the effectiveness and robustness of the method proposed in this paper are verified, and the influence of the coaxial circle’s spatial structure on the method is explored through simulations of and experiments on a drogue model. Under the interference of a large amount of noise, the duality elimination success rate of our method can also be maintained at a level that is more than 10% higher than others. In addition, the accuracy of the normal vector obtained by the fusion algorithm is improved, and the mean angle error is reduced by more than 26.7%. Full article

(This article belongs to the Special Issue Navigation and Perception Sensors and Systems for Robots, Autonomous Vehicles, and Wearable Technology)

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16 pages, 1761 KiB

Open AccessCommunication

Graph SLAM Built over Point Clouds Matching for Robot Localization in Tunnels

by Carlos Prados Sesmero, Sergio Villanueva Lorente and Mario Di Castro

Sensors 2021, 21(16), 5340; https://doi.org/10.3390/s21165340 - 7 Aug 2021

Cited by 13 | Viewed by 4174

Abstract

This paper presents a fully original algorithm of graph SLAM developed for multiple environments—in particular, for tunnel applications where the paucity of features and the difficult distinction between different positions in the environment is a problem to be solved. This algorithm is modular, generic, and expandable to all types of sensors based on point clouds generation. The algorithm may be used for environmental reconstruction to generate precise models of the surroundings. The structure of the algorithm includes three main modules. One module estimates the initial position of the sensor or the robot, while another improves the previous estimation using point clouds. The last module generates an over-constraint graph that includes the point clouds, the sensor or the robot trajectory, as well as the relation between positions in the trajectory and the loop closures. Full article

(This article belongs to the Special Issue Navigation and Perception Sensors and Systems for Robots, Autonomous Vehicles, and Wearable Technology)

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20 pages, 8934 KiB

Open AccessArticle

Applying a ToF/IMU-Based Multi-Sensor Fusion Architecture in Pedestrian Indoor Navigation Methods

by Farzan Farhangian, Mohammad Sefidgar and Rene Jr. Landry

Sensors 2021, 21(11), 3615; https://doi.org/10.3390/s21113615 - 22 May 2021

Cited by 7 | Viewed by 5841

Abstract

The advancement of indoor Inertial Navigation Systems (INS) based on the low-cost Inertial Measurement Units (IMU) has been long reviewed in the field of pedestrian localization. There are various sources of error in these systems which lead to unstable and unreliable positioning results, especially in long term performances. These inaccuracies are usually caused by imperfect system modeling, inappropriate sensor fusion models, heading drift, biases of IMUs, and calibration methods. This article addresses the issues surrounding unreliability of the low-cost Micro-Electro-Mechanical System (MEMS)-based pedestrian INS. We designed a novel multi-sensor fusion method based on a Time of Flight (ToF) distance sensor and dual chest- and foot-mounted IMUs, aided by an online calibration technique. An Extended Kalman Filter (EKF) is accounted for estimating the attitude, position, and velocity errors, as well as estimation of IMU biases. A fusion architecture is derived to provide a consistent velocity measurement by operative contribution of ToF distance sensor and foot mounted IMU. In this method, the measurements of the ToF distance sensor are used for the time-steps in which the Zero Velocity Update (ZUPT) measurements are not active. In parallel, the chest mounted IMU is accounted for attitude estimation of the pedestrian’s chest. As well, by designing a novel corridor detection filter, the heading drift is restricted in each straightway. Compared to the common INS method, developed system proves promising and resilient results in two-dimensional corridor spaces for durations of up to 11 min. Finally, the results of our experiments showed the position RMS error of less than 3 m and final-point error of less than 5 m. Full article

(This article belongs to the Special Issue Navigation and Perception Sensors and Systems for Robots, Autonomous Vehicles, and Wearable Technology)

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25 pages, 1075 KiB

Open AccessReview

The Implementation of Precise Point Positioning (PPP): A Comprehensive Review

by Mohamed Elsheikh, Umar Iqbal, Aboelmagd Noureldin and Michael Korenberg

Sensors 2023, 23(21), 8874; https://doi.org/10.3390/s23218874 - 31 Oct 2023

Cited by 18 | Viewed by 7583

Abstract

High-precision positioning from Global Navigation Satellite Systems (GNSS) has garnered increased interest due to growing demand in various applications, like autonomous car navigation and precision agriculture. Precise Point Positioning (PPP) offers a distinct advantage over differential techniques by enabling precise position determination of a GNSS rover receiver through the use of external corrections sourced from either the Internet or dedicated correction satellites. However, PPP’s implementation has been challenging due to the need to mitigate numerous GNSS error sources, many of which are eliminated in differential techniques such as Real-Time Kinematics (RTK) or overlooked in Standard Point Positioning (SPP). This paper extensively reviews PPP’s error sources, such as ionospheric delays, tropospheric delays, satellite orbit and clock errors, phase and code biases, and site displacement effects. Additionally, this article examines various PPP models and correction sources that can be employed to address these errors. A detailed discussion is provided on implementing the standard dual-frequency (DF)-PPP to achieve centimeter- or millimeter-level positioning accuracy. This paper includes experimental examples of PPP implementation results using static data from the International GNSS Service (IGS) station network and a kinematic road test based on the actual trajectory to showcase DF-PPP development for practical applications. By providing a fusion of theoretical insights with practical demonstrations, this comprehensive review offers readers a pragmatic perspective on the evolving field of Precise Point Positioning. Full article

(This article belongs to the Special Issue Navigation and Perception Sensors and Systems for Robots, Autonomous Vehicles, and Wearable Technology)

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