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

Han, Jiaming; Yang, Zhong; Hu, Guoxiong; Fang, Ting; Xu, Hao

doi:10.1108/sr-02-2018-0024

Cited by 7 publications

(3 citation statements)

References 32 publications

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“…The results of different algorithms are calculated in an 10-bin histogram as shown in Fig. 8, where the horizontal axis denotes NormDist, and the vertical axis gives the number of images in each histogram bin [37]- [39]. We just take count of the NormDist in the interval [0, 0.01], and place the error into the NormDist = 0.01 when the NormDist ≥ 0.01.…”

Section: Results and Analysis A Experiments About Vanishing Point mentioning

confidence: 99%

An Improved Phase Correlation Method for Stop Detection of Autonomous Driving

Zhu

2020

IEEE Access

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Simultaneous Localization and Mapping (SLAM) is the process by which a mobile robot carrying specific sensors builds a map of the environment and at the same time uses this map to estimate its pose. Currently, SLAM has been proven its value and is a hot topic. However, challenges still exist: when the mobile robot stops in the process of motion and a large number of feature points in the environment move slightly, the feature matching process cannot eliminate the non-stationary feature point pairs. The introduction of a large number of outliers (non-stationary feature point pairs) seriously affects the observation process of SLAM. It directly leads to the estimation errors of mobile robot pose and the 3D features position, and further leads to keyframe trajectory drift. If there is a mechanism that allows the robot to accurately detect itself in a stop state, then the pose and map points could be locked, and the state variables could be optimized to make the system enter the positive succession. Therefore, detecting the stop status of the mobile robot is a significant work for SLAM. In this manuscript, an improved phase correlation method is proposed to solve the problem of stop detection for the autonomous driving vehicle in the dynamic street environment. After experiments, it is revealed that the stop detection is significant for the performance improvement to the state-of-the-art visual SLAM system, and the improved phase correlation method has higher stop detection accuracy than the conventional phase correlation in various scenarios. INDEX TERMS Stop detection, phase correlation, simultaneous localization and mapping, vanishing point.

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Section: Results and Analysis A Experiments About Vanishing Point mentioning

confidence: 99%

An Improved Phase Correlation Method for Stop Detection of Autonomous Driving

Zhu

2020

IEEE Access

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“…Aiming at this problem, other studies had been made. In [24], the VP voting strategy was developed on the basis of the road region estimation using manifold ranking. In [8], the VP detection was based on the road border region estimation.…”

Section: Related Workmentioning

confidence: 99%

Unstructured road detection via combining the model‐based and feature‐based methods

Yang

et al. 2019

IET Intelligent Transport Systems

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“…The reliable perception of the environment is essential for the diverse robotics tasks (Murphy and Murphy, 2000;Bloss, 2008;Ivancsits and Ricky Lee, 2013;Han et al, 2018). The reliability of the generated maps depends on sensor accuracy, ambient environmental condition, extrinsic and intrinsic parameters, sensor reliability, etc.…”

Section: Introductionmentioning

confidence: 99%

A modified sensor fusion framework for quantifying and removing the effect of harsh environmental condition for reliable mobile robot mapping

Singh

Nagla

2019

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Purpose An autonomous mobile robot requires efficient perception of the environment to perform various tasks in a challenging environment. The precise sensory information from the range sensors is required to accomplish prerequisites, such as SLAM, path planning and localization. But the accuracy and precision of the sensors become unreliable in harsh environmental conditions because of the effect of rain, dust, humidity, fog and smoke. The purpose of this paper is to generate robust mapping of the environment in harsh environmental conditions. Design/methodology/approach This paper presents a novel technique, averaging data with short range selection (ADWSRS), to reduce the effect of harsh environmental (rain, wind, humidity, etc.) conditions on sensory information (range) to generate reliable grid mapping. The sensory information on laser and sonar sensors in terms of probability values (occupied/unoccupied cell) in generating grid maps are fused after passing through two newly designed pre-processing filters: laser averaging filter and short range selection filter. This proposed approach relies on various aspects such as averaging laser data analogous to current pose of the sensor, selection of short range with respect to threshold value to remove the effect of specular reflection/crosstalk of sonar and a newly designed apparatus in which dirt cover (glass cover) and air blower are coupled to remove the influence of dirt, rain and humidity. Findings This proposed approach is tested in different environmental conditions, and to verify the consistency of the proposed approach, qualitative and quantitative analyses are carried out, which shows 42.5 per cent improvement in the probability value of occupied cells in the generated grid map. Originality/value The proposed ADWSRS approach reduced the effect of harsh environmental conditions such as fog, rain and smoke to generate efficient mapping of the environment, which may be implemented in diverse applications such as autonomous navigation, localization, path planning and mapping.

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

Cited by 7 publications

References 32 publications

An Improved Phase Correlation Method for Stop Detection of Autonomous Driving

An Improved Phase Correlation Method for Stop Detection of Autonomous Driving

Unstructured road detection via combining the model‐based and feature‐based methods

A modified sensor fusion framework for quantifying and removing the effect of harsh environmental condition for reliable mobile robot mapping

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