Accurate and Robust Vanishing Point Detection Method in Unstructured Road Scenes

Han, Jiaming; Yang, Zhong; Hu, Guoxiong; Zhang, Tianyi; Song, Jiarong

doi:10.1007/s10846-018-0814-8

Cited by 8 publications

(4 citation statements)

References 25 publications

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“…Besides, we think that it is more reasonable to use the term "drivable region estimation" than the term "road region estimation". To estimate the drivable region, we refer to the work of Zhou et al (2004), Yang et al (2013), Zou et al (2016) and follow the idea of Han et al (2018), then make a further improvement. First, the patches on the bottom boundary of the road image are separated into the drivable region query nodes and the background patches by regarding the feature vector of the bottom-center patch as the benchmark (calculate the scaled feature vector Euclidean distance between the bottom-center patch with the other patches on the bottom boundary of image in this work), as shown in the first row of the second column in Figure 2.…”

Section: Drivable Region Estimationmentioning

confidence: 99%

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Robust and efficient vanishing point detection in unstructured road scenes for assistive navigation

Han

Yang

et al. 2019

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Purpose This paper aims to propose a robust and efficient method for vanishing point detection in unstructured road scenes. Design/methodology/approach The proposed method includes two main stages: drivable region estimation and vanishing point detection. In drivable region estimation stage, the road image is segmented into a set of patches; then the drivable region is estimated by the patch-wise manifold ranking. In vanishing point detection stage, the LSD method is used to extract the straight lines; then a series of principles are proposed to remove the noise lines. Finally, the vanishing point is detected by a novel voting strategy. Findings The proposed method is validated on various unstructured road images collected from the real world. It is more robust and more efficient than the state-of-the-art method and the other three recent methods. Experimental results demonstrate that the detected vanishing point is practical for vision-sensor-based navigation in complex unstructured road scenes. Originality/value This paper proposes a patch-wise manifold ranking method to estimate the drivable region that contains most of the informative clues for vanishing point detection. Based on the removal of the noise lines through a series of principles, a novel voting strategy is proposed to detect the vanishing point.

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Section: Drivable Region Estimationmentioning

confidence: 99%

“…In this work, an undirected graph G = (V,E) is constructed. Different from the work of Zou et al (2016) and Han et al (2018), nodes V are set to be the 20 Â 20 patches generated by evenly dividing the road image to reduce the computational complexity. Each patch in our graph is connected to the patches in its 3 Â 3 neigh-boring region.…”

Section: Introductionmentioning

confidence: 99%

Robust and efficient vanishing point detection in unstructured road scenes for assistive navigation

Han

Yang

et al. 2019

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“…However, for completely unstructured roads with unclear edges, this method has several limitations [7][8][9][10]. Vanishing point detection is also a commonly used method for identifying unstructured roads [11][12][13][14]. Ende Wang proposed a method of light normalization based on RGB (Red, Green and Blue) images [15].…”

Section: Introductionmentioning

confidence: 99%

Detection and Modeling of Unstructured Roads in Forest Areas Based on Visual-2D Lidar Data Fusion

Lei

Yao

Zhao

et al. 2021

Forests

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The detection and recognition of unstructured roads in forest environments are critical for smart forestry technology. Forest roads lack effective reference objects and manual signs and have high degrees of nonlinearity and uncertainty, which pose severe challenges to forest engineering vehicles. This research aims to improve the automation and intelligence of forestry engineering and proposes an unstructured road detection and recognition method based on a combination of image processing and 2D lidar detection. This method uses the “improved SEEDS + Support Vector Machine (SVM)” strategy to quickly classify and recognize the road area in the image. Combined with the remapping of 2D lidar point cloud data on the image, the actual navigation requirements of forest unmanned navigation vehicles were fully considered, and road model construction based on the vehicle coordinate system was achieved. The algorithm was transplanted to a self-built intelligent navigation platform to verify its feasibility and effectiveness. The experimental results show that under low-speed conditions, the system can meet the real-time requirements of processing data at an average of 10 frames/s. For the centerline of the road model, the matching error between the image and lidar is no more than 0.119 m. The algorithm can provide effective support for the identification of unstructured roads in forest areas. This technology has important application value for forestry engineering vehicles in autonomous inspection and spraying, nursery stock harvesting, skidding, and transportation.

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“…Vanishing point detection in the unstructured road that lacks road markings is more challenging and requires more complex background suppression and an effective voting strategy [5]. Some previous work tries to circumvent the need for a geometric feature-based detection method by doing motionbased methods of vanishing point detection.…”

Section: Introductionmentioning

confidence: 99%

Vision-based vanishing point detection of autonomous navigation of mobile robot for outdoor applications

Rusli

Nurhalim

Rusyadi

2021

J. Mechatron. Electr. Power Veh. Technol.

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The vision-based approach to mobile robot navigation is considered superior due to its affordability. This paper aims to design and construct an autonomous mobile robot with a vision-based system for outdoor navigation. This robot receives inputs from camera and ultrasonic sensor. The camera is used to detect vanishing points and obstacles from the road. The vanishing point is used to detect the heading of the road. Lines are extracted from the environment using a canny edge detector and Houghline Transforms from OpenCV to navigate the system. Then, removed lines are processed to locate the vanishing point and the road angle. A low pass filter is then applied to detect a vanishing point better. The robot is tested to run in several outdoor conditions such as asphalt roads and pedestrian roads to follow the detected vanishing point. By implementing a Simple Blob Detector from OpenCV and ultrasonic sensor module, the obstacle's position in front of the robot is detected. The test results show that the robot can avoid obstacles while following the heading of the road in outdoor environments. Vision-based vanishing point detection is successfully applied for outdoor applications of autonomous mobile robot navigation.

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Accurate and Robust Vanishing Point Detection Method in Unstructured Road Scenes

Cited by 8 publications

References 25 publications

Robust and efficient vanishing point detection in unstructured road scenes for assistive navigation

Robust and efficient vanishing point detection in unstructured road scenes for assistive navigation

Detection and Modeling of Unstructured Roads in Forest Areas Based on Visual-2D Lidar Data Fusion

Vision-based vanishing point detection of autonomous navigation of mobile robot for outdoor applications

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