Confocal Ellipse Hough Transform for Polarization Compass in the Nonideal Atmosphere

Wang, Jue; Hu, Pengwei; Qian, Jianqiang; Guo, Lei

doi:10.1109/tim.2023.3256476

Cited by 8 publications

(4 citation statements)

References 22 publications

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“…The Berry model, more accurate than the Rayleigh model, can be useful for better heading measurements [ 199 , 200 ] relying on neutral points detection [ 201 ] but cannot be fully used as a pattern prediction model because the positions of neutral points are strongly modified by air pollution, clouds, and debris from large volcanic eruptions [ 187 , 202 ], and because it lacks accuracy near the sun and the horizon. A recent study [ 203 ], considered the influence of the solar altitude angle on the neutral point position variations to model the pattern of polarized skylight.…”

Section: Polarized Vision For Robotics Navigationmentioning

confidence: 99%

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Passive Polarized Vision for Autonomous Vehicles: A Review

Serres,

Lapray,

Viollet

et al. 2024

Sensors

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This review article aims to address common research questions in passive polarized vision for robotics. What kind of polarization sensing can we embed into robots? Can we find our geolocation and true north heading by detecting light scattering from the sky as animals do? How should polarization images be related to the physical properties of reflecting surfaces in the context of scene understanding? This review article is divided into three main sections to address these questions, as well as to assist roboticists in identifying future directions in passive polarized vision for robotics. After an introduction, three key interconnected areas will be covered in the following sections: embedded polarization imaging; polarized vision for robotics navigation; and polarized vision for scene understanding. We will then discuss how polarized vision, a type of vision commonly used in the animal kingdom, should be implemented in robotics; this type of vision has not yet been exploited in robotics service. Passive polarized vision could be a supplemental perceptive modality of localization techniques to complement and reinforce more conventional ones.

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Section: Polarized Vision For Robotics Navigationmentioning

confidence: 99%

“…Other studies exploit the symmetry pattern of the

[ 172 ], also called the ∞ characteristic model [ 225 ]. Similarly, a few studies have applied the Hough transform to the

pattern [ 200 , 226 ].…”

Section: Polarized Vision For Robotics Navigationmentioning

confidence: 99%

Passive Polarized Vision for Autonomous Vehicles: A Review

Serres,

Lapray,

Viollet

et al. 2024

Sensors

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“…Then, the control board will release control signals to control the two DC motors and the servo motor so that the ESDV can run stably in the lane. Traditional lane boundary calculation uses Hough line detection [22][23][24], wherein changing white lane configurations lead to fluctuating slope values, enabling the ESDV to discern between straight paths and curves. Thus, the self-driving vehicle can operate stably in the lane.…”

Section: Image Recognition System Description 221 Image Adjustment An...mentioning

confidence: 99%

“…In this study, the ESDV uses the secondary camera to identify the sign marks on the road (as shown in Figure 5) and then transmits the captured image data to the miniature computer for analysis before further transmitting it to the control board. Lastly, the control board shows the information to the LED display, allowing the driver or road users to know the real-time condition of Traditional lane boundary calculation uses Hough line detection [22][23][24], wherein changing white lane configurations lead to fluctuating slope values, enabling the ESDV to discern between straight paths and curves. Thus, the self-driving vehicle can operate stably in the lane.…”

Section: Sign Mark Recognitionmentioning

confidence: 99%

Applying a Novel Image Recognition Curve-Fitting Control Strategy Combined with a Cloud Monitoring Technique into an Electric Self-Driving Vehicle (ESDV) to Improve Its Operation Efficiency

Liu,

Lin,

Lai

et al. 2023

Processes

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This study aims to develop an image recognition curve-fitting (IRCF) control strategy integrated with a cloud monitoring technique for application in electric self-driving vehicles (ESDVs) to improve their operation efficiency. The study focuses on an electric vehicle designed to reduce the carbon emissions and promote sustainability. The main camera, combined with the IRCF control strategy, was used to control the ESDV to enhance its operational efficiency. The proposed ESDV employs a pair of cameras to capture images and transmit them to the cloud-based web monitoring platform in real time. This allows the researchers to adjust the control parameters and promptly remove the road obstacles. The ESDV is equipped with a horn, two ultrasonic sensors, and an LED display, which can instantly detect the obstacles ahead of and behind the vehicle. When there are obstacles on the road, the vehicle will automatically stop, and the LED display will provide a visual representation of the obstacles, accompanied by the sounding of the horn as a warning signal. Meanwhile, the secondary camera detects the signal mark and feeds it back to the LED display, thereby informing passengers and other road users about the prevailing driving conditions. The proposed IRCF control strategy was compared with the traditional Hough line detection method on a 110 m ring road. The results revealed that the proposed control strategy outperformed the traditional Hough line detection method in terms of speed, efficiency, and running dexterity. Therefore, integrating the proposed control strategy into the automatic assistance driving system can improve the ESDV’s operation efficiency. Furthermore, the combination of the obstacle detection and signal sign detection functions for the ESDV used in this study can better fulfill the actual ESDV operation requirements on the road.

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