Improved Multi-Sensor Fusion Positioning System Based on GNSS/LiDAR/Vision/IMU With Semi-Tight Coupling and Graph Optimization in GNSS Challenging Environments

Zhu, Jiaming; Zhou, Han; Wang, Ziyi; Yang, Suli

doi:10.1109/access.2023.3311359

Cited by 6 publications

(5 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Like our proposed research, in [9], the authors presented a multi-sensor fusion to estimate the vehicle's trajectory utilizing an odometer, IMU, and vision camera for vehicle localization, achieving a low MSE score when comparing the pose estimation. However, multi-sensor (GNSS, IMU LiDAR and visual camera) approaches require high processing and power demands, as demonstrated in [24]. In contrast, our work used GPS and an RGB-D sensor for vehicle localization in exclusive lanes, dealing with fewer processing and synchronization requirements.…”

Section: Discussionmentioning

confidence: 99%

“…Multi-sensor data are used for simultaneous localization and mapping (SLAM), representing the fundamental tasks to solve in autonomous vehicles. SLAM consists of constructing a map of an unknown environment while simultaneously inferring the vehicle's pose within the map [24]. The primary sensors employed for SLAM are LiDAR and cameras.…”

Section: Introductionmentioning

confidence: 99%

“…In [26], a vSLAM-based localization system for indoors creates the environment map and localizes the camera pose in the maps. In [24], an improved multi-sensor fusion positioning system based on GNSS, LiDAR, cameras, and IMU with graph optimization is presented to improve the accuracy of vehicle pose. Similarly, the authors of [27] propose a novel visual-inertial simultaneous localization and mapping (VI-SLAM) method for intelligent navigation systems to overcome the posed challenges of car dynamics or large-scale outdoor environments.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Vision-Based Object Localization and Classification for Electric Vehicle Driving Assistance

Medina-Garcia,

Duarte-Jasso,

Cardenas-Cornejo

et al. 2023

Smart Cities

View full text Add to dashboard Cite

The continuous advances in intelligent systems and cutting-edge technology have greatly influenced the development of intelligent vehicles. Recently, integrating multiple sensors in cars has improved and spread the advanced drive-assistance systems (ADAS) solutions for achieving the goal of total autonomy. Despite current self-driving approaches and systems, autonomous driving is still an open research issue that must guarantee the safety and reliability of drivers. This work employs images from two cameras and Global Positioning System (GPS) data to propose a 3D vision-based object localization and classification method for assisting a car during driving. The experimental platform is a prototype of a two-sitter electric vehicle designed and assembled for navigating the campus under controlled mobility conditions. Simultaneously, color and depth images from the primary camera are combined to extract 2D features, which are reprojected into 3D space. Road detection and depth features isolate point clouds representing the objects to construct the occupancy map of the environment. A convolutional neural network was trained to classify typical urban objects in the color images. Experimental tests validate car and object pose in the occupancy map for different scenarios, reinforcing the car position visually estimated with GPS measurements.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Vision-Based Object Localization and Classification for Electric Vehicle Driving Assistance

Medina-Garcia,

Duarte-Jasso,

Cardenas-Cornejo

et al. 2023

Smart Cities

View full text Add to dashboard Cite

show abstract

“…Under the effective constraints of the INS, more reliable gross error detection can be achieved, exhibiting a good complementarity between the two systems. Among the combinations of the two, there are common configurations such as loosely coupled (LC), tightly coupled (TC), deeply coupled (DC) [6], and the recently proposed semi-tightly coupled (STC) [7]. In all these configurations, the INS serves to constrain the positioning results of the GNSS.…”

Section: Introductionmentioning

confidence: 99%

Semi-Tightly Coupled Robust Model for GNSS/UWB/INS Integrated Positioning in Challenging Environments

Sun,

Gao,

Tao

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

Currently, the integration of the Global Navigation Satellite System (GNSS), Ultra-Wideband (UWB), and Inertial Navigation System (INS) has become a reliable positioning method for outdoor dynamic vehicular and airborne applications, enabling high-precision and continuous positioning in complex environments. However, environmental interference and limitations of single positioning sources pose challenges. Especially in areas with limited access to satellites and UWB base stations, loosely coupled frameworks for GNSS/INS and UWB/INS are insufficient to support robust estimation. Furthermore, within a tightly coupled framework, parameter estimations from different sources can interfere with each other, and errors in computation can easily contaminate the entire positioning estimator. To balance robustness and stability in integrated positioning, this paper proposes a comprehensive quality control method. This method is based on the semi-tightly coupled concept, utilizing the INS position information and considering the dilution of precision (DOP) skillfully to achieve complementary advantages in GNSS/UWB/INS integrated positioning. In this research, reliable position and variance information obtained by INS are utilized to provide a priori references for a robust estimation of the original data from GNSS and UWB, achieving finer robustness without increasing system coupling, which fully demonstrates the advantages of semi-tight integration. Based on self-collected data, the effectiveness and superiority of the proposed quality control strategy are validated under severely occluded environments. The experimental results demonstrate that the semi-tightly coupled robust estimation method proposed in this paper is capable of accurately identifying gross errors in GNSS and UWB observation data, and it has a significant effect on improving positioning accuracy and smoothing trajectories. Additionally, based on the judgment of the DOP, this method can ensure the output of continuous and reliable positioning results in complex and variable environments. Verified by actual data, under the conditions of severe sky occlusion and NLOS (Non-Line-of-Sight), compared with the loosely coupled GNSS/INS, the positioning accuracy in the E, N, U directions of the semi-tight coupled GNSS/INS proposed in this paper has improved by 37%, 46%, and 28%. Compared with the loosely coupled UWB/INS, the accuracy in the E and N directions of the semi-tight coupled UWB/INS has improved by 60% and 34%. In such environments, GNSS employs the RTD (Real-Time Differential) algorithm, UWB utilizes the two-dimensional plane-positioning algorithm, and the positioning accuracy of the semi-tight coupled robust model of GNSS/UWB/INS in the E, N, U directions is 0.42 m, 0.55 m, and 3.20 m respectively.

show abstract

“…As the number of satellites in orbit increases, the precision of satellite navigation has greatly improved. However, satellite navigation is susceptible to harsh environments, such as tunnels and urban canyons [2][3][4]. Cooperative vehicle infrastructure systems (CVIS) are widely applied to make up for the shortcomings of satellite positioning [5].…”

Section: Introductionmentioning

confidence: 99%

Self-Position Determination Based on Array Signal Subspace Fitting under Multipath Environments

Cao,

Li,

Tang

et al. 2023

Sensors

View full text Add to dashboard Cite

A vehicle’s position can be estimated with array receiving signal data without the help of satellite navigation. However, traditional array self-position determination methods are faced with the risk of failure under multipath environments. To deal with this problem, an array signal subspace fitting method is proposed for suppressing the multipath effect. Firstly, all signal incidence angles are estimated with enhanced spatial smoothing and root multiple signal classification (Root-MUSIC). Then, non-line-of-sight (NLOS) components are distinguished from multipath signals using a K-means clustering algorithm. Finally, the signal subspace fitting (SSF) function with a P matrix is established to reduce the NLOS components in multipath signals. Meanwhile, based on the initial clustering estimation, the search area can be significantly reduced, which can lead to less computational complexity. Compared with the C-matrix, oblique projection, initial signal fitting (ISF), multiple signal classification (MUSIC) and signal subspace fitting (SSF), the simulated experiments indicate that the proposed method has better NLOS component suppression performance, less computational complexity and more accurate positioning precision. A numerical analysis shows that the complexity of the proposed method has been reduced by at least 7.64dB. A cumulative distribution function (CDF) analysis demonstrates that the estimation accuracy of the proposed method is increased by 3.10dB compared with the clustering algorithm and 11.77dB compared with MUSIC, ISF and SSF under multipath environments.

show abstract

Improved Multi-Sensor Fusion Positioning System Based on GNSS/LiDAR/Vision/IMU With Semi-Tight Coupling and Graph Optimization in GNSS Challenging Environments

Cited by 6 publications

References 34 publications

Vision-Based Object Localization and Classification for Electric Vehicle Driving Assistance

Vision-Based Object Localization and Classification for Electric Vehicle Driving Assistance

Semi-Tightly Coupled Robust Model for GNSS/UWB/INS Integrated Positioning in Challenging Environments

Self-Position Determination Based on Array Signal Subspace Fitting under Multipath Environments

Contact Info

Product

Resources

About