Precision and Reliability of Tightly Coupled PPP GNSS and Landmark Monocular Vision Positioning

Pang, Menglin; Tiberius, C.C.J.M.

doi:10.3390/s20051537

Cited by 3 publications

(1 citation statement)

References 23 publications

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“…The target image acquisition device is composed of four parts, acquisition hardware, bracket, light source, laser range finder and image processing system [41,42] . The image acquisition system was assembled by a CCD camera-Vie MV-EM200C (resolution 1600×1200, frame rate 40 fps).…”

Section: Otsu Threshold Segmentation Effect and Adaboost Classificatimentioning

confidence: 99%

Monocular vision and calculation of regular three-dimensional target pose based on Otsu and Haar-feature AdaBoost classifier

Wang

Wei-liang

et al. 2020

International Journal of Agricultural and Biological Engineering

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Using machine vision to identify and sort scattered regular targets is an urgent problem to be solved in automated production lines. This study proposed a three-dimensional (3D) recognition method combining monocular vision and machine learning algorithms. According to the color characteristics of the targets, to convert the original color picture into YC b C r mode and use the 2D Otsu algorithm to perform gray level image segmentation on the C b channel. Then the Haar-feature training was carried out. The comparison of feature training and Haar method for Hough transform showed that the recognized time of Haar-feature AdaBoost trainer reached 31.00 ms, while its false recognized rate was 3.91%. The strong classifier was formed by weight combination, and the Hough contour transformation algorithm was set to correct the normal vector between plane coordinate and camera coordinate system. The monocular vision system ensured that the field of camera view had not obstructed while the dots were being struck. It was measured and calculated angles between targets and the horizontal plane which coordinate points of the identified plane feature. The testing results were compared with the Otsu and AdaBoost trainer where the prediction and training set have an error of no more than 0.25 mm. Its correct rate can reach 95%. It shows that the Otsu and Haar-feature based on AdaBoost algorithm is feasible within a certain error ranges and meet the engineering requirements for solving the poses of automated regular three-dimensional targets.

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Section: Otsu Threshold Segmentation Effect and Adaboost Classificatimentioning

confidence: 99%

Monocular vision and calculation of regular three-dimensional target pose based on Otsu and Haar-feature AdaBoost classifier

Wang

Wei-liang

et al. 2020

International Journal of Agricultural and Biological Engineering

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Transmission corridor line safety monitoring method based on monocular vision

Wang

Shang

Guo³

et al. 2021

J. Phys.: Conf. Ser.

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Aiming at the problem of low accuracy of traditional methods in transmission corridor line safety monitoring, a transmission corridor line safety monitoring method based on monocular vision is designed. First of all, this article defines the target object of the safety monitoring of the transmission corridor line. Then, based on the monocular vision measurement to obtain the internal parameters of the camera geometry, optical characteristics, etc., the position of the power transmission corridor line security surveillance camera is calibrated, the power transmission corridor line security monitoring graphics are displayed, and the security monitoring data of the transmission corridor line is used for perception and track.Finally, the safety monitoring and early warning of transmission corridor lines are realized. The experimental results show that the monitoring accuracy of the design method is significantly higher than that of the control group, which can solve the problem of low accuracy of transmission corridor line safety monitoring by traditional methods.

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Localization Integrity for Intelligent Vehicles Through Fault Detection and Position Error Characterization

Hage¹,

Xu²,

Ibañez‐Guzmán

2022

IEEE Trans. Intell. Transport. Syst.

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Localization integrity consists in providing a realtime measure of the level of trust to be placed in the localization estimates as vehicles operate. It provides a means of knowing whether position estimates are usable for navigation purposes. This paper formalizes the integrity concept and its underlying principles. Vehicles operate in different navigation environments, and so multiple sensors are used to ensure the required performance. Different sources of error exist. They must be bounded according to the acceptable level of risk for the application.This paper presents a generic approach for addressing integrity. It combines measurement rejection (for measurements considered to be faults) and position error characterization. For this purpose, a multi-sensor data fusion with a Fault Detection and Exclusion algorithm is constituted using a bank of information filters. These filters allow detected faults to be isolated without any prior assumption regarding the number of simultaneous errors. In addition, external integrity is expressed as a Protection Level of the localization solution. It uses a Student's t-distribution in order to bound the distribution of the position error applicable to small integrity risks after a learning step. The approach is tested on data acquired on public roads using an experimental vehicle equipped with off-the-shelf proprioceptive and exteroceptive sensors together with an HD map. The results obtained validate the proposed approach.

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Precision and Reliability of Tightly Coupled PPP GNSS and Landmark Monocular Vision Positioning

Cited by 3 publications

References 23 publications

Monocular vision and calculation of regular three-dimensional target pose based on Otsu and Haar-feature AdaBoost classifier

Monocular vision and calculation of regular three-dimensional target pose based on Otsu and Haar-feature AdaBoost classifier

Transmission corridor line safety monitoring method based on monocular vision

Localization Integrity for Intelligent Vehicles Through Fault Detection and Position Error Characterization

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