Remote Sensing

Research

22 pages, 6263 KiB

Open AccessArticle

Intelligent Environment-Adaptive GNSS/INS Integrated Positioning with Factor Graph Optimization

by Zhengdao Li, Pin-Hsun Lee, Tsz Hin Marcus Hung, Guohao Zhang and Li-Ta Hsu

Remote Sens. 2024, 16(1), 181; https://doi.org/10.3390/rs16010181 - 31 Dec 2023

Viewed by 1554

Global navigation satellite systems (GNSSs) applied to intelligent transport systems in urban areas suffer from multipath and non-line-of-sight (NLOS) effects due to the signal reflections from high-rise buildings, which seriously degrade the accuracy and reliability of vehicles in real-time applications. Accordingly, the integration [...] Read more.

Global navigation satellite systems (GNSSs) applied to intelligent transport systems in urban areas suffer from multipath and non-line-of-sight (NLOS) effects due to the signal reflections from high-rise buildings, which seriously degrade the accuracy and reliability of vehicles in real-time applications. Accordingly, the integration between GNSS and inertial navigation systems (INSs) could be utilized to improve positioning performance. However, the fixed GNSS solution uncertainty of the conventional integration method cannot determine the fluctuating GNSS reliability in fast-changing urban environments. This weakness becomes solvable using a deep learning model for sensing the ambient environment intelligently, and it can be further mitigated using factor graph optimization (FGO), which is capable of generating robust solutions based on historical data. This paper mainly develops the adaptive GNSS/INS loosely coupled system on FGO, along with the fixed-gain Kalman filter (KF) and adaptive KF (AKF) being taken as comparisons. The adaptation is aided by a convolutional neural network (CNN), and the feasibility is verified using data from different grades of receivers. Compared with the integration using fixed-gain KF, the proposed adaptive FGO (AFGO) maintains the 100% positioning availability and reduces the overall 2D positioning error by up to 70% in the aspects of both root mean square error (RMSE) and standard deviation (STD). Full article

(This article belongs to the Special Issue Remote Sensing in Urban Positioning and Navigation)

► Show Figures

Figure 1

18 pages, 5430 KiB

Open AccessArticle

Localization of Mobile Robots Based on Depth Camera

by Zuoliang Yin, Huaizhi Wen, Wei Nie and Mu Zhou

Remote Sens. 2023, 15(16), 4016; https://doi.org/10.3390/rs15164016 - 14 Aug 2023

Viewed by 1073

Abstract

In scenarios of indoor localization of mobile robots, Global Positioning System (GPS) signals are prone to loss due to interference from urban building environments and cannot meet the needs of robot localization. On the other hand, traditional indoor localization methods based on wireless [...] Read more.

In scenarios of indoor localization of mobile robots, Global Positioning System (GPS) signals are prone to loss due to interference from urban building environments and cannot meet the needs of robot localization. On the other hand, traditional indoor localization methods based on wireless signals such as Bluetooth and WiFi often require the deployment of multiple devices in advance, and these methods can only obtain distance information and are unable to obtain the attitude of the positioning target in space. This paper proposes a method for the indoor localization of mobile robots based on a depth camera. Firstly, we extracted ORB feature points from images captured by a depth camera and performed homogenization processing. Then, we performed feature matching between adjacent two frames of images, and the mismatched points are eliminated to improve the accuracy of feature matching. Finally, we used the Iterative Closest Point (ICP) algorithm to estimate the camera’s pose, thus achieving the localization of mobile robots in indoor environments. In addition, an experimental evaluation was conducted on the TUM dataset of the Technical University of Munich to validate the feasibility of the proposed depth-camera-based indoor localization system for mobile robots. The experimental results show that the average localization accuracy of our algorithm on three datasets is 0.027 m, which can meet the needs of indoor localization for mobile robots. Full article

(This article belongs to the Special Issue Remote Sensing in Urban Positioning and Navigation)

► Show Figures

Figure 1

26 pages, 10332 KiB

Open AccessArticle

Improving the Accuracy of Vehicle Position in an Urban Environment Using the Outlier Mitigation Algorithm Based on GNSS Multi-Position Clustering

by Hak Ju Kim, Yong Hun Kim, Joo Han Lee, So Jin Park, Bo Sung Ko and Jin Woo Song

Remote Sens. 2023, 15(15), 3791; https://doi.org/10.3390/rs15153791 - 30 Jul 2023

Cited by 1 | Viewed by 1211

Abstract

In this paper, we propose a multi-position cluster-based weighted position estimation method that minimizes the influence of multipath (MP)/non-line-of-sight (NLOS) signals using a global navigation satellite system (GNSS) receiver. The proposed method is suitable for positioning passenger cars, particularly in urban driving environments. [...] Read more.

In this paper, we propose a multi-position cluster-based weighted position estimation method that minimizes the influence of multipath (MP)/non-line-of-sight (NLOS) signals using a global navigation satellite system (GNSS) receiver. The proposed method is suitable for positioning passenger cars, particularly in urban driving environments. Density-based spatial clustering of applications with noise (DBSCAN)-based clustering is performed by generating multi-position data through a subset of observable satellites and analyzing the density characteristics of position data generated by line-of-sight (LOS) satellite signals from the generated multi-position data. To estimate the optimal position through clustered data, we propose a method by constructing a weighted model through Doppler-based velocity measurements, which is robust to MP delay signals compared to code-based pseudorange measurements. In addition, to prevent unnecessary clustering points from having weights, the predicted range is selected based on the nonholonomic constraint of the vehicle. The proposed algorithm was quantitatively validated by selecting a region in an actual urban environment where the MP/NLOS error could occur significantly. It was confirmed that the accuracy of vehicle position was improved by approximately 24% by the proposed method compared to existing positioning solutions. Full article

(This article belongs to the Special Issue Remote Sensing in Urban Positioning and Navigation)

► Show Figures

Figure 1

28 pages, 3904 KiB

Open AccessArticle

Accurate and Robust Rotation-Invariant Estimation for High-Precision Outdoor AR Geo-Registration

by Kejia Huang, Chenliang Wang and Wenjiao Shi

Remote Sens. 2023, 15(15), 3709; https://doi.org/10.3390/rs15153709 - 25 Jul 2023

Cited by 1 | Viewed by 1225

Abstract

Geographic registration (geo-registration) is a crucial foundation for augmented reality (AR) map applications. However, existing methods encounter difficulties in aligning spatial data with the ground surface in complex outdoor scenarios. These challenges make it difficult to accurately estimate the geographic north orientation. Consequently, [...] Read more.

Geographic registration (geo-registration) is a crucial foundation for augmented reality (AR) map applications. However, existing methods encounter difficulties in aligning spatial data with the ground surface in complex outdoor scenarios. These challenges make it difficult to accurately estimate the geographic north orientation. Consequently, the accuracy and robustness of these methods are limited. To overcome these challenges, this paper proposes a rotation-invariant estimation method for high-precision geo-registration in AR maps. The method introduces several innovations. Firstly, it improves the accuracy of generating heading data from low-cost hardware by utilizing Real-Time Kinematic GPS and visual-inertial fusion. This improvement contributes to the increased stability and precise alignment of virtual objects in complex environments. Secondly, a fusion method combines the true-north direction vector and the gravity vector to eliminate alignment errors between geospatial data and the ground surface. Lastly, the proposed method dynamically combines the initial attitude relative to the geographic north direction with the motion-estimated attitude using visual-inertial fusion. This approach significantly reduces the requirements on sensor hardware quality and calibration accuracy, making it applicable to various AR precision systems such as smartphones and augmented reality glasses. The experimental results show that this method achieves AR geo-registration accuracy at the 0.1-degree level, which is about twice as high as traditional AR geo-registration methods. Additionally, it exhibits better robustness for AR applications in complex scenarios. Full article

(This article belongs to the Special Issue Remote Sensing in Urban Positioning and Navigation)

► Show Figures

Figure 1

17 pages, 4499 KiB

Open AccessArticle

GM(1,1)-Based Weighted K-Nearest Neighbor Algorithm for Indoor Localization

by Lai Xiang, Ying Xu, Jianhui Cui, Yang Liu, Ruozhou Wang and Guofeng Li

Remote Sens. 2023, 15(15), 3706; https://doi.org/10.3390/rs15153706 - 25 Jul 2023

Viewed by 865

Abstract

Along with the IoT technology, the importance of indoor positioning is increasing, but the accuracy of the traditional fingerprint positioning algorithm is negatively affected by the complex indoor environment. This issue of low indoor spatial geolocation localization accuracy when the signal is collected [...] Read more.

Along with the IoT technology, the importance of indoor positioning is increasing, but the accuracy of the traditional fingerprint positioning algorithm is negatively affected by the complex indoor environment. This issue of low indoor spatial geolocation localization accuracy when the signal is collected away from the present stage occurs due to the signal instability of the iBeacon in the traditional fingerprint localization algorithm, which generates a variety of factors such as object blocking and reflection, multipath effect, etc., as well as the scarcity of reference fingerprint data points. In response, this study proposes an inverse distance-weighted optimization WKNN algorithm for indoor localization based on the GM(1,1) model. By implementing GM(1,1) model pre-process leveling, the original fingerprint library was reconstructed into a large-capacity fingerprint database using the inverse distance-weighted interpolation method. The local inverse distance-weighted interpolation was used for interpolation, combined with the WKNN algorithm to complete the coordinate solution in real time. This effectively solved the issue of low localization accuracy caused by the large fluctuation of the received signal strength (RSS) sampling measurement data and the existence of few reference fingerprint datapoints in the fingerprint database. The results show that this algorithm reduced the average positioning error by 5.9% compared with ordinary kriging (OK) interpolation leveling and reduced the average positioning error by 18.2% compared with the indoor spatial location accuracy of the original fingerprint database, which can effectively improve the positioning accuracy and provide technical support for indoor location and navigation services. Full article

(This article belongs to the Special Issue Remote Sensing in Urban Positioning and Navigation)

► Show Figures

Figure 1

20 pages, 6961 KiB

Open AccessArticle

High-Accuracy Positioning in GNSS-Blocked Areas by Using the MSCKF-Based SF-RTK/IMU/Camera Tight Integration

by Qiaozhuang Xu, Zhouzheng Gao, Cheng Yang and Jie Lv

Remote Sens. 2023, 15(12), 3005; https://doi.org/10.3390/rs15123005 - 8 Jun 2023

Cited by 4 | Viewed by 1868

Abstract

The integration of global navigation satellite system (GNSS) single-frequency (SF) real-time kinematics (RTKs) and inertial navigation system (INS) has the advantages of low-cost and low-power consumption compared to the multiple-frequency GNSS RTK/INS integration system. However, due to the vulnerability of GNSS signal reception, [...] Read more.

The integration of global navigation satellite system (GNSS) single-frequency (SF) real-time kinematics (RTKs) and inertial navigation system (INS) has the advantages of low-cost and low-power consumption compared to the multiple-frequency GNSS RTK/INS integration system. However, due to the vulnerability of GNSS signal reception, the application of the GNSS SF-RTK/INS integration is limited in complex environments. To improve the positioning accuracy of SF-RTK/INS integration in GNSS-blocked environments, we present a low-cost tight integration system based on BDS/GPS SF-RTK, a low-cost inertial measurement unit (IMU), and a monocular camera. In such a system, a multi-state constraint Kalman filter (MSCKF) is adopted to integrate the single-frequency pseudo-range, phase-carrier, inertial measurements, and visual data tightly. A wheel robot dataset collected under satellite signal-blocked conditions is used to evaluate its performance in terms of position, attitude, and run time, respectively. Results illustrated that the presented model can provide higher position accuracy compared to those provided by the RTK/INS tight integration system and visual-inertial tight integration system. Moreover, the average running time presents the potential of the presented method in real-time applications. Full article

(This article belongs to the Special Issue Remote Sensing in Urban Positioning and Navigation)

► Show Figures

Graphical abstract

17 pages, 6395 KiB

Open AccessArticle

An In-Vehicle Smartphone RTK/DR Positioning Method Combined with OSM Road Network

by Fuyou Wang, Chengfa Gao, Rui Shang, Ruicheng Zhang, Lu Gan, Qi Liu and Jianchao Wang

Remote Sens. 2023, 15(2), 398; https://doi.org/10.3390/rs15020398 - 9 Jan 2023

Cited by 2 | Viewed by 1494

Abstract

In vehicle navigation scenarios, the RTK positioning results of smartphones are prone to jumps due to the interference of complex urban environments, and the heading angle of dead reckoning (DR) is prone to divergence. In order to obtain more stable and high-precision smartphone [...] Read more.

In vehicle navigation scenarios, the RTK positioning results of smartphones are prone to jumps due to the interference of complex urban environments, and the heading angle of dead reckoning (DR) is prone to divergence. In order to obtain more stable and high-precision smartphone positioning results, this paper proposes an RTK/DR positioning method combined with the OpenStreetMap road network. The OpenStreetMap road network data are used to correct the heading angle during the linear motion phase to improve heading angle accuracy. In order to reduce the impact of RTK results jumping on subsequent DR, it is possible to set up a measurement update switch, which combines the RTK covariance matrix, vehicle motion state, and RTK heading angle change information to determine whether to perform a measurement update. The research uses two smartphones to carry out four vehicle positioning tests. The eight sets of test results show that the heading angle correction method based on the OpenStreetMap road network can effectively control the accumulation of heading angle errors and allow DR trajectory to be more consistent with the benchmark. Compared with RTK, the forward accuracy of RTK/DR positioning method is almost unchanged, even though the direction accuracy and lateral positioning accuracy are significantly improved. The RTK/DR horizontal positioning accuracy of both smartphones is approximately 1.3 m, which is better rather than the RTK results. The proposed RTK/DR positioning method can obtain more reliable orientation and position information than RTK. Full article

(This article belongs to the Special Issue Remote Sensing in Urban Positioning and Navigation)

► Show Figures

Figure 1

30 pages, 11551 KiB

Open AccessArticle

WiFi Access Points Line-of-Sight Detection for Indoor Positioning Using the Signal Round Trip Time

by Xu Feng, Khuong An Nguyen and Zhiyuan Luo

Remote Sens. 2022, 14(23), 6052; https://doi.org/10.3390/rs14236052 - 29 Nov 2022

Cited by 11 | Viewed by 2487

Abstract

The emerging WiFi Round Trip Time measured by the IEEE 802.11mc standard promised sub-meter-level accuracy for WiFi-based indoor positioning systems, under the assumption of an ideal line-of-sight path to the user. However, most workplaces with furniture and complex interiors cause the wireless signals [...] Read more.

The emerging WiFi Round Trip Time measured by the IEEE 802.11mc standard promised sub-meter-level accuracy for WiFi-based indoor positioning systems, under the assumption of an ideal line-of-sight path to the user. However, most workplaces with furniture and complex interiors cause the wireless signals to reflect, attenuate, and diffract in different directions. Therefore, detecting the non-line-of-sight condition of WiFi Access Points is crucial for enhancing the performance of indoor positioning systems. To this end, we propose a novel feature selection algorithm for non-line-of-sight identification of the WiFi Access Points. Using the WiFi Received Signal Strength and Round Trip Time as inputs, our algorithm employs multi-scale selection and Machine Learning-based weighting methods to choose the most optimal feature sets. We evaluate the algorithm on a complex campus WiFi dataset to demonstrate a detection accuracy of 93% for all 13 Access Points using 34 out of 130 features and only 3 s of test samples at any given time. For individual Access Point line-of-sight identification, our algorithm achieved an accuracy of up to 98%. Finally, we make the dataset available publicly for further research. Full article

(This article belongs to the Special Issue Remote Sensing in Urban Positioning and Navigation)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Remote Sensing in Urban Positioning and Navigation

Share This Special Issue

Special Issue Editor

Special Issue Information

Keywords

Published Papers (8 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI