Design of a Navigation System Based on Scene Matching and Software in the Loop Simulation

Shahoud, Ayham; Shashev, Dmitriy; Shidlovskiy, Stanislav

doi:10.1109/icit52682.2021.9491778

Cited by 5 publications

(4 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Image matching could be realized either based on extreme correlation techniques, such as cross-correlation, or using feature-matching techniques [ 16 ]. Correlation-based techniques require image alignment, i.e., other sensors are needed to rotate and scale both images to have the same scale and orientation.…”

Section: Reducing the Search Space In The Feature-matching Processmentioning

confidence: 99%

Mutual-Aided INS/Vision Navigation System Analysis and Optimization Using Sequential Filtering with State Recalculation

Shahoud

Shashev

Shidlovskiy

2022

Sensors

Self Cite

View full text Add to dashboard Cite

This paper presents the implementation of a mutual-aided navigation system for an aerial vehicle. Employing all available sensors in navigation is effective at maintaining continuous and optimal results. The images offer a lot of information about the surrounding environment, but image processing is time-consuming and causes timing problems. While traditional fusion algorithms tend to reduce the delay errors or ignore them, this research depends on state estimation recalculation during the delay time and on sequential filtering. To reduce the image matching time, the map is processed offline, then key point clusters are stored to avoid feature recalculation online. The sensors’ information is used to bound the search space for the matched features on the map, then they are reprojected on the captured images to exclude the unuseful part from processing. The suggested mutual-aided form compensates for the inertial system drift, which enhances the system’s accuracy and independence. The system was tested using data collected from a real flight using a DJI drone. The measurements from an inertial measurement unit (IMU), camera, barometer, and magnetometer were fused using a sequential Kalman Filter. The final results prove the efficiency of the suggested system to navigate with high independency, with an RMS position error of less than 3.5 m.

show abstract

Section: Reducing the Search Space In The Feature-matching Processmentioning

confidence: 99%

Mutual-Aided INS/Vision Navigation System Analysis and Optimization Using Sequential Filtering with State Recalculation

Shahoud

Shashev

Shidlovskiy

2022

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…The alignment process was done using the heading angle 'ψ' from the compass and the height 'h' from an altimeter. A detailed explanation of the navigation system implementation is presented in [10]. Let 'T' be the captured image and 'I' the reference image, the normalized cross-correlation is given in the following equation:…”

Section: Navigation Algorithmmentioning

confidence: 99%

“…To reduce matching time, only a cropped window of the map was matched with the captured image. The size of the cropped window changes adaptively with the last movement amplitude and the scaled image size [10]. The cross-correlation process consumes less time than the classification process.…”

Section: Navigation Algorithmmentioning

confidence: 99%

Detection of Good Matching Areas Using Convolutional Neural Networks in Scene Matching-Based Navigation Systems

Shahoud

Shashev

Shidlovskiy

2021

Proceedings of the 31th International Conference on Computer Graphics and Vision. Volume 2

Self Cite

View full text Add to dashboard Cite

This paper presents a solution for false matching detection in scene matching-based aerial navigation systems. A navigation system that uses normalized cross-correlation to match a captured image with a reference image was designed. While traditional methods rely on statistical indicators to detect false matchings, this research relied on deep learning using Convolutional Neural Network (CNN). A CNN was trained to online predict the probability of a matching result to be true or false. The training dataset of images was constructed depending on the knowledge of where good matching areas are expected to be. The probability numbers were stored as an assistant map to be used again with the same reference map without classification. The system was implemented and tested in a 3D simulation environment using models for a drone, camera, and flight environment. The Robot Operating System (ROS) and the 3D dynamic simulator Gazebo were used for simulation. The results proved the efficiency of the proposed method in excluding the false matchings. Using the assistant map without classification resulted in an execution time of 41ms and RMS error of position less than 1.2m.

show abstract

“…As a visual navigation and positioning technology, scene-matching navigation [7][8][9] has the advantages of strong autonomy, high terminal guidance accuracy, no accumulation of errors with the voyage, and strong anti-interference ability. It is often combined with INS and GNSS to improve the anti-interference ability and achieve medium and long-range navigation.…”

Section: Introductionmentioning

confidence: 99%

Suitable-Matching Areas’ Selection Method Based on Multi-Level Saliency

Jiang,

Luo,

Liu

2023

Remote Sensing

View full text Add to dashboard Cite

Scene-matching navigation is one of the essential technologies for achieving precise navigation in satellite-denied environments. Selecting suitable-matching areas is crucial for planning trajectory and reducing yaw. Most traditional selection methods of suitable-matching areas use hierarchical screening based on multiple feature indicators. However, these methods rarely consider the interrelationship between different feature indicators and use the same set of screening thresholds for different categories of images, which has poor versatility and can easily cause mis-selection and omission. To solve this problem, a suitable-matching areas’ selection method based on multi-level saliency is proposed. The matching performance score is obtained by fusing several segmentation levels’ salient feature extraction results and performing weighted calculations with the sub-image edge density. Compared with the hierarchical screening methods, the matching performance of the candidate areas selected by our algorithm is at least 22.2% higher, and it also has a better matching ability in different scene categories. In addition, the number of missed and wrong selections is significantly reduced. The average matching accuracy of the top three areas selected by our method reached 0.8549, 0.7993, and 0.7803, respectively, under the verification of multiple matching algorithms. Experimental results show this paper’s suitable-matching areas’ selection method is more robust.

show abstract

Design of a Navigation System Based on Scene Matching and Software in the Loop Simulation

Cited by 5 publications

References 10 publications

Mutual-Aided INS/Vision Navigation System Analysis and Optimization Using Sequential Filtering with State Recalculation

Mutual-Aided INS/Vision Navigation System Analysis and Optimization Using Sequential Filtering with State Recalculation

Detection of Good Matching Areas Using Convolutional Neural Networks in Scene Matching-Based Navigation Systems

Suitable-Matching Areas’ Selection Method Based on Multi-Level Saliency

Contact Info

Product

Resources

About