Small obstacle detection using stereo vision for autonomous ground vehicle

Gupta, Krishnam; Upadhyay, Sarthak; Gandhi, Vineet; Krishna, K. Madhav

doi:10.1145/3132446.3134889

Cited by 4 publications

(3 citation statements)

References 18 publications

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“…Abiyev et al [12] proposed an obstacle detection and pathfinding algorithm using SVM (Support Vector Machine) and A* algorithms in mobile robots. Gupta et al [13] integrated the appearance of the image and 3D cues (e.g. image gradient, curvature potential, and depth variance) into the MRF (Markov Random Field) formula to identify the area of obstacles.…”

Section: Related Workmentioning

confidence: 99%

“…Due to the characteristics of radio waves, this method only locates the positions or recognizes the behaviors of obstacles, and usually need complex equipment. In the image-based method, monocular or binocular cameras are usually used to capture the surrounding environment, and then machine learning algorithms (e.g., the deformable grid method [9], the convolutional neural network [10], the image segmentation [11], the support vector machine (SVM) [12], or the Markov chain [13], etc.) are used to process and extract the information of obstacles in the images, thereby locating the position and size of the obstacle.…”

Section: Introductionmentioning

confidence: 99%

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A Position and Area Localization Algorithm for Obstacles in the Environment of Sparsely-Deployed Sensors

Gao

Yang

et al. 2021

IEEE Access

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Measuring the space area of obstacles is one of the important problems in obstacle localizing fields. Most of the existing research works on the localization of obstacles focus on where the obstacles are, and few of them measure both the positions and the areas of the obstacles. In this paper, we propose a Minimum convex bounding Polygon localizing algorithm based on Visible light Tracking (MPVT) in order to rapidly and accurately locate the position and area of a 2D obstacle in the environment of sparsely-deployed sensors. MPVT first determines the initial localizing light by Visible Light Tracing method (VLT). Second, it searches the first side of the Minimum Convex Bounding Polygon (MCBP) of the obstacle. Third, MPVT calculates the subsequent other sides and the vertexes of MCBP until the next side coincides with the first side. In order to evaluate the approximation degree between the actual values and the localization values in terms of areas, positions and shapes, we propose two performance evaluation indexes, i.e., the area ratio and the ratio of equivalent radius. We conducted experiments on the influence of obstacle orientation and sparseness of sensor deployment, the accuracy comparison with the existing methods, and the time complexity. Experiment results show that MPVT can accurately locate the position and area of the obstacle in the environment of sparsely-deployed sensors with low time overhead, and is suitable for low-cost obstacle localization applications. INDEX TERMS Minimum convex bounding polygon, obstacle localization, ratio of equivalent radius, sparse environment, visible light tracking

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Position and Area Localization Algorithm for Obstacles in the Environment of Sparsely-Deployed Sensors

Gao

Yang

et al. 2021

IEEE Access

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show abstract

“…: Prabhakar et al (2017)). There are also several other approaches that use combinations between different methods, such as: Gupta et al (2017) which uses methods based on Neural Networks, Stereo Vision and Image Segmentation, and Giosan and Nedevschi (2014) which uses methods based upon Stereo Vision, Optical Flow and Image Segmentation.…”

Section: Introductionmentioning

confidence: 99%

Road obstacles positional and dynamic features extraction combining object detection, stereo disparity maps and optical flow data

Rateke,

von Wangenheim

2020

Preprint

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One of the most relevant tasks in an intelligent vehicle navigation system is the detection of obstacles. It is important that a visual perception system for navigation purposes identifies obstacles, and it is also important that this system can extract essential information that may influence the vehicle's behavior, whether it will be generating an alert for a human driver or guide an autonomous vehicle in order to be able to make its driving decisions. In this paper we present an approach for the identification of obstacles and extraction of class, position, depth and motion information from these objects that employs data gained exclusively from passive vision. We performed our experiments on two different data-sets and the results obtained shown a good efficacy from the use of depth and motion patterns to assess the obstacles' potential threat status.

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Road obstacles positional and dynamic features extraction combining object detection, stereo disparity maps and optical flow data

Rateke

Wangenheim

2020

Machine Vision and Applications

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Small obstacle detection using stereo vision for autonomous ground vehicle

Cited by 4 publications

References 18 publications

A Position and Area Localization Algorithm for Obstacles in the Environment of Sparsely-Deployed Sensors

A Position and Area Localization Algorithm for Obstacles in the Environment of Sparsely-Deployed Sensors

Road obstacles positional and dynamic features extraction combining object detection, stereo disparity maps and optical flow data

Road obstacles positional and dynamic features extraction combining object detection, stereo disparity maps and optical flow data

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