Position and Attitude Estimation Method Integrating Visual Odometer and GPS

Yu, Yang; Shen, Qiang; Li, Jie; Deng, Zilong; Wang, Hanyu; Gao, Xiao

doi:10.3390/s20072121

Cited by 11 publications

(6 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Visual odometer has become popular in current navigation systems [41] as it allows us to estimate the relative transformation between two stereo frames, assuming a known baseline. Based on this relative transformation information, we can get the current position of the target.…”

Section: ) Visual Odometer Factormentioning

confidence: 99%

A Multi-Sensor Information Fusion Method Based on Factor Graph for Integrated Navigation System

Yang

Sun

et al. 2021

IEEE Access

View full text Add to dashboard Cite

The current navigation systems used in many autonomous mobile robotic applications, like unmanned vehicles, are always equipped with various sensors to get accurate navigation results. The key point is to fuse the information from different sensors efficiently. However, different sensors provide asynchronous measurements, some of which even appear to be nonlinear. Moreover, some sensors are vulnerable in specific environments, e.g., GPS signal is likely to work poorly in interior space, underground, and tall buildings. We propose a multi-sensor information fusion method based on a factor graph to fuse all available asynchronous sensor information and efficiently and accurately calculate a navigation solution. Assuming the sensor measurements and navigation states in a navigation system as factor nodes and variable nodes in a factor graph, respectively, the update of the states can be implemented in the framework of the factor graph. The proposed method is experimentally validated using two different datasets. A comparison with Federated Filter, which has been widely used in integrated navigation systems, demonstrates the proposed method's effectiveness. Additionally, analyzing the navigation results with data loss verifies that the proposed method could achieve sensor plug and play in software.

show abstract

Section: ) Visual Odometer Factormentioning

confidence: 99%

A Multi-Sensor Information Fusion Method Based on Factor Graph for Integrated Navigation System

Yang

Sun

et al. 2021

IEEE Access

View full text Add to dashboard Cite

show abstract

“…However, the GNSS signals are vulnerable to obstructions and interferences, so their performance needs to be improved in the GNSS challenging environment. There have been quite a few attempts to integrate GNSS with the vision-based localization approaches, which provide position increments most likely using inertial sensors and/or different odometers [25]. On the other hand, position and velocity information from GNSS can also be used for global optimization and geo-referencing in visual SLAM computation [26].…”

Section: Gnss/vision Combined Localizationmentioning

confidence: 99%

A Resilient Smartphone Positioning Approach by Tightly Integrating the Monocular Camera and GNSS Signals

Xu,

Wang,

Chen

et al. 2021

JGPS

View full text Add to dashboard Cite

The Global Navigation Satellite System (GNSS) signals are often blocked or interfered in complex geographical or electromagnetic environments, which may make GNSS receivers unable to provide satisfying navigation and positioning services. There have been many ground-based or space-based GNSS augmentation systems to improve the resilience of GNSS positioning, of which most of them rely on additional infrastructures. In this study, a smartphone-based tightly-coupled positioning method was developed using the images from a build-in monocular camera and GNSS signals. In this method, the feature points with the known coordinates are regarded as 'visual pseudolite' and the distance between the camera and the feature points was calculated according to the photogrammetry approaches and used to estimate the user positioning with GNSS signals. The experimental results showed the feasibility of the tightly-coupled positioning algorithm and reached the positioning accuracy of ±5.56 m (1?), which is significantly higher than that of GNSS-only and vision-only positioning solutions.

show abstract

“…Researchers also call this problem “scale ambiguity”. When the monocular camera is moving, the error caused by scale ambiguity will keep accumulating to lead to a scale drift problem [ 7 , 8 , 9 ]. This problem makes it difficult to obtain realistic flight altitude estimation results directly.…”

Section: Introductionmentioning

confidence: 99%

VIAE-Net: An End-to-End Altitude Estimation through Monocular Vision and Inertial Feature Fusion Neural Networks for UAV Autonomous Landing

Zhang

et al. 2021

Sensors

View full text Add to dashboard Cite

Altitude estimation is one of the fundamental tasks of unmanned aerial vehicle (UAV) automatic navigation, where it aims to accurately and robustly estimate the relative altitude between the UAV and specific areas. However, most methods rely on auxiliary signal reception or expensive equipment, which are not always available, or applicable owing to signal interference, cost or power-consuming limitations in real application scenarios. In addition, fixed-wing UAVs have more complex kinematic models than vertical take-off and landing UAVs. Therefore, an altitude estimation method which can be robustly applied in a GPS denied environment for fixed-wing UAVs must be considered. In this paper, we present a method for high-precision altitude estimation that combines the vision information from a monocular camera and poses information from the inertial measurement unit (IMU) through a novel end-to-end deep neural network architecture. Our method has numerous advantages over existing approaches. First, we utilize the visual-inertial information and physics-based reasoning to build an ideal altitude model that provides general applicability and data efficiency for neural network learning. A further advantage is that we have designed a novel feature fusion module to simplify the tedious manual calibration and synchronization of the camera and IMU, which are required for the standard visual or visual-inertial methods to obtain the data association for altitude estimation modeling. Finally, the proposed method was evaluated, and validated using real flight data obtained during a fixed-wing UAV landing phase. The results show the average estimation error of our method is less than 3% of the actual altitude, which vastly improves the altitude estimation accuracy compared to other visual and visual-inertial based methods.

show abstract

Position and Attitude Estimation Method Integrating Visual Odometer and GPS

Cited by 11 publications

References 25 publications

A Multi-Sensor Information Fusion Method Based on Factor Graph for Integrated Navigation System

A Multi-Sensor Information Fusion Method Based on Factor Graph for Integrated Navigation System

A Resilient Smartphone Positioning Approach by Tightly Integrating the Monocular Camera and GNSS Signals

VIAE-Net: An End-to-End Altitude Estimation through Monocular Vision and Inertial Feature Fusion Neural Networks for UAV Autonomous Landing

Contact Info

Product

Resources

About