Moving Obstacles Detection and Camera Pointing for Mobile Robot Applications

Lakrouf, Mustapha; Larnier, Stanislas; Devy, Michel; Achour, Nouara

doi:10.1145/3068796.3068816

Cited by 11 publications

(8 citation statements)

References 9 publications

(11 reference statements)

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“…The combined use of area-scan camera and 2D laser rangefinder (LRF) is widespread in numerous fields, such as robotics [1], [2], object detection [3], 3D reconstruction [4], autonomous vehicles [5], [6] and localization and mapping [7]. This combination is not only low-cost, but also can obtain rich visual information like color and texture in the scene, as well as the direct geometric structure information like coordinate and distance.…”

Section: Introductionmentioning

confidence: 99%

The Extrinsic Calibration of Area-Scan Camera and 2D Laser Rangefinder (LRF) Using Checkerboard Trihedron

et al. 2020

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The combination of area-scan camera and 2D laser rangefinder (LRF) can capture both textural and geometrical information from a scene at the same time, and has been widely used in various fields. Due to the differences of the installation position and acquisition mode, calibrating the extrinsic parameters, including the rotation and translation, of two sensors is necessary for fusing the camera image and LRF data. In this paper, a simple and flexible extrinsic calibration method is proposed by only acquiring a checkerboard trihedron once. Using checkerboard trihedron as a mobile referenced control field, the proposed method includes three steps to calibrate the extrinsic parameters. First, the rotation and translation between the trihedron and LRF are solved with a simplified perspective-three-point (P3P) solution; Second, using the collinear equation of checkerboard corners and their pixels in the image, the camera is calibrated with respect to the trihedron; Third, combining the last two steps, the rotation and translation parameters between the camera and LRF are finally calibrated with the intermediate referenced trihedron. After a lot of simulation and real experiments, the proposed method has been demonstrated to have the advantages of simple operation, strong robustness and high accuracy in real experiment. INDEX TERMS Area-scan camera, 2D laser rangefinder, checkerboard trihedron, collinear equation, extrinsic calibration, P3P.

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Section: Introductionmentioning

confidence: 99%

The Extrinsic Calibration of Area-Scan Camera and 2D Laser Rangefinder (LRF) Using Checkerboard Trihedron

et al. 2020

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“…The literature shows an effort to add vision-based systems to mobile robot applications. The manuscript [ 24 ] presents a system for the detection of persons using cascade classifiers. This approach uses the implementation of HOG (Histogram of Oriented Gradients) feature descriptor and AdaBoost algorithm with a 2D laser and a camera.…”

Section: Introductionmentioning

confidence: 99%

A Distributed Vision-Based Navigation System for Khepera IV Mobile Robots

Farías

Fábregas

Torres

et al. 2020

Sensors

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This work presents the development and implementation of a distributed navigation system based on object recognition algorithms. The main goal is to introduce advanced algorithms for image processing and artificial intelligence techniques for teaching control of mobile robots. The autonomous system consists of a wheeled mobile robot with an integrated color camera. The robot navigates through a laboratory scenario where the track and several traffic signals must be detected and recognized by using the images acquired with its on-board camera. The images are sent to a computer server that performs a computer vision algorithm to recognize the objects. The computer calculates the corresponding speeds of the robot according to the object detected. The speeds are sent back to the robot, which acts to carry out the corresponding manoeuvre. Three different algorithms have been tested in simulation and a practical mobile robot laboratory. The results show an average of 84% success rate for object recognition in experiments with the real mobile robot platform.

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“…In the literature, you can find some research articles related to vision-based systems using different techniques. For example in [21], the authors present a system for the detection of persons using cascade classifiers. Its method uses the implementation of HOG (Histogram of Oriented Gradients) feature descriptor and AdaBoost algorithm with a 2D laser and a camera.…”

Section: Introductionmentioning

confidence: 99%

A Distributed Vision-Based Navigation System for Khepera IV Mobile Robots

Farías¹,

Fábregas²,

Torres³

et al. 2020

Preprint

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This work presents the development and implementation of a distributed navigation system based on computer vision. The autonomous system consists of a wheeled mobile robot with an integrated colour camera. The robot navigates through a laboratory scenario where the track and several traffic signals must be detected and recognized by using the images acquired with its on-board camera. The images are sent to a computer server that processes them and calculates the corresponding speeds of the robot using a cascade of trained classifiers. These speeds are sent back to the robot, which acts to carry out the corresponding manoeuvre. The classifier cascade should be trained before experimentation with two sets of positive and negative images. The number of images in these sets should be considered to limit the training stage time and avoid overtraining the system.

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Moving Obstacles Detection and Camera Pointing for Mobile Robot Applications

Cited by 11 publications

References 9 publications

The Extrinsic Calibration of Area-Scan Camera and 2D Laser Rangefinder (LRF) Using Checkerboard Trihedron

The Extrinsic Calibration of Area-Scan Camera and 2D Laser Rangefinder (LRF) Using Checkerboard Trihedron

A Distributed Vision-Based Navigation System for Khepera IV Mobile Robots

A Distributed Vision-Based Navigation System for Khepera IV Mobile Robots

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