A new road tracking method based on heading direction detection

Guo, Xiaoxin; Li, Qun; Sun, Chao

doi:10.1177/0954407017740791

Cited by 4 publications

(5 citation statements)

References 25 publications

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“…In perspective geometry, the radial center of the optical flow field is the focus of expansion (FOE). As states Hashimoto et al (1996); Guo et al (2018), the FOE is equivalent to the VP. Therefore, the nearest VP candidate to the LMCC is elected the principal VP.…”

Section: Vanishing Points Votingmentioning

confidence: 99%

Void detection for UAV based on optical flow and vanishing points

Oliveira

Sousa

Morais

et al. 2021

Procedings Do XV Simpósio Brasileiro De Automação Inteligente

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Indoor micro aerial vehicle (MAV) navigation is mostly based on high-computational cost obstacle detection algorithms. In this paper, we propose a path planning framework based on perspective cues. The vanishing points were detected by using the Hough-Canny transform. Due to the cluttering, multiple vanishing points candidates are arranged according to the proximity to the optical flow focus of expansion. Obstacles' depths are detected via the optical flow vector clustering. When multiple planes are recognized, the elected vanishing point is considered the center of the furthest empty plane from the camera. This remark guides the direction of the MAV to a particularly free-of-obstacles void area. Preliminary experimental results indicate that the proposed method is faster than other visual-based navigation algorithms.

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Section: Vanishing Points Votingmentioning

confidence: 99%

Void detection for UAV based on optical flow and vanishing points

Oliveira

Sousa

Morais

et al. 2021

Procedings Do XV Simpósio Brasileiro De Automação Inteligente

View full text Add to dashboard Cite

show abstract

“…Non-urban navigation is more challenging, since it assumes there are no lane markers, and it is difficult to distinguish the road from surrounding vegetation. In this context, works such as [1], [3]- [5] use Gabor filters to segment the road using texture, [6] uses a genetic algorithm (MPGA) to classify road super-pixels, [7] uses the relative position of the focus of expansion and the road vp to get the road direction using optical flow, and [13] uses multivariate Gaussian approaches to extract vps.…”

Section: Introductionmentioning

confidence: 99%

Autonavi3at Software Interface to Autonomously Navigate on Urban Roads Using Omnidirectional Vision and a Mobile Robot

Martínez

Bacca-Cortes

2022

Cien.Ing.Neogranadina

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The design of efficient autonomous navigation systems for mobile robots or autonomous vehicles is fundamental to perform the programmed tasks. Basically, two kind of sensors are used in urban road following: LIDAR and cameras. LIDAR sensors are highly accurate but expensive and extra work is needed for human understanding of the point cloud scenes; however, visual content is understood better by human beings, which should be used to develop human-robot interfaces. In this work, a computer vision-based urban road following software tool called AutoNavi3AT for mobile robots and autonomous vehicles is presented. The urban road following scheme proposed in AutoNavi3AT uses vanishing point estimation and tracking on panoramic images to control the mobile robot heading on the urban road. To do that, Gabor filters, region growing, and particle filters were used. In addition, laser range data are also employed for local obstacle avoidance. Quantitative results were achieved using two kind of tests, one uses datasets acquired at the Universidad del Valle campus, and field tests using a Pioneer 3AT mobile robot. As a result, important improvements in the vanishing point estimation of 68.26 % and 61.46 % in average were achieved, which is useful for mobile robots and autonomous vehicles when they are moving on urban roads.

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“…Among a lot of VPs presented in every frame of the driving videos, the most special VP is the intersection point of lanes or road boundaries. Hereafter, the VP is called R-VP (road VP) [ 2 ] in the rest of this paper.…”

Section: Introductionmentioning

confidence: 99%

“…In other words, the FOE formation of a host vehicle does not depend on geometrical feature extractions from lanes, road boundaries, and other lines. Furthermore, if a host vehicle moves in parallel with road boundaries and the roads are almost flat, its FOE will coincide with R-VP [ 2 ]. That is why FOEs of host vehicles can be used to detect efficiently R-VPs for both structured roads and unstructured roads in the existing motion-based R-VP detection methods.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Robust Road Vanishing Point Detection Adapted to the Real-world Driving Scenes

Khac

Choi

Park

et al. 2021

Sensors

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Vanishing point (VP) provides extremely useful information related to roads in driving scenes for advanced driver assistance systems (ADAS) and autonomous vehicles. Existing VP detection methods for driving scenes still have not achieved sufficiently high accuracy and robustness to apply for real-world driving scenes. This paper proposes a robust motion-based road VP detection method to compensate for the deficiencies. For such purposes, three main processing steps often used in the existing road VP detection methods are carefully examined. Based on the analysis, stable motion detection, stationary point-based motion vector selection, and angle-based RANSAC (RANdom SAmple Consensus) voting are proposed. A ground-truth driving dataset including various objects and illuminations is used to verify the robustness and real-time capability of the proposed method. The experimental results show that the proposed method outperforms the existing motion-based and edge-based road VP detection methods for various illumination conditioned driving scenes.

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A new road tracking method based on heading direction detection

Cited by 4 publications

References 25 publications

Void detection for UAV based on optical flow and vanishing points

Void detection for UAV based on optical flow and vanishing points

Autonavi3at Software Interface to Autonomously Navigate on Urban Roads Using Omnidirectional Vision and a Mobile Robot

A Robust Road Vanishing Point Detection Adapted to the Real-world Driving Scenes

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