Vision System of Mobile Robot Combining Binocular and Depth Cameras

Yang, Yuxiang; Meng, Xiang; Gao, Mingyu

doi:10.1155/2017/4562934

Cited by 13 publications

(8 citation statements)

References 21 publications

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“…Binocular stereo vision uses two cameras to estimate location of a mobile robot. Although it has the advantage of better performance in the regard of accuracy, but it is more expensive and complex to compute [67]. While monocular vision on the other hand is simple to set-up and of low cost.…”

Section: B Monocular Vision Positioing Systemmentioning

confidence: 99%

A Review on Challenges of Autonomous Mobile Robot and Sensor Fusion Methods

2020

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Autonomous mobile robots are becoming more prominent in recent time because of their relevance and applications to the world today. Their ability to navigate in an environment without a need for physical or electro-mechanical guidance devices has made it more promising and useful. The use of autonomous mobile robots is emerging in different sectors such as companies, industries, hospital, institutions, agriculture and homes to improve services and daily activities. Due to technology advancement, the demand for mobile robot has increased due to the task they perform and services they render such as carrying heavy objects, monitoring, search and rescue missions, etc. Various studies have been carried out by researchers on the importance of mobile robot, its applications and challenges. This survey paper unravels the current literatures, the challenges mobile robot is being faced with. A comprehensive study on devices/sensors and prevalent sensor fusion techniques developed for tackling issues like localization, estimation and navigation in mobile robot are presented as well in which they are organised according to relevance, strengths and weaknesses. The study therefore gives good direction for further investigation on developing methods to deal with the discrepancies faced with autonomous mobile robot.

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Section: B Monocular Vision Positioing Systemmentioning

confidence: 99%

A Review on Challenges of Autonomous Mobile Robot and Sensor Fusion Methods

2020

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“…Let us assume that the first camera is aligned with the world reference system with the second camera offset first by a rotation R and then by a translation T. This sets out the matrices for the image projection to be: (𝑋 𝐶 , 𝑌 𝐶 , 𝑍 𝐶 ) is the corresponding coordinate in the colour camera coordinate system [12] Based on the principle of small hole imaging,…”

Section: Epipolar Geometrymentioning

confidence: 99%

Binocular Vision-Based Intelligent 3-D Perception for Robotics Application

Karunachandra¹,

Herath²

2021

Preprint

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Vision-based robotics has been the subject of several research contributions in the area of vision and control. Vision technology is becoming a pioneer in the most common applications such as localization, automated map creation, autonomous navigation, mapping analysis, or risk pattern prediction. The Stereo applications or programs use pairs of 2-D images as inputs and generate reconstructed 3-D imagery by locating the matching points. This paper introduces a method to the development of an algorithm of intelligent 3-D view reconstruction using binocular vision for robotic applications. The proposed system consists of two identical colour cameras and cameras were mounted as one stereo camera. 3-D reconstruction and visualization were performed according to the pair of 2-D images. Calibration, multi-view image acquisition, the stereo rectification process, and the disparity process were discussed in Sect. 2. Real-time captured images and stereo images from Middlebury Stereo Datasets were used to test the system and verify results in Sect. 3.

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“…Singh Mahesh Kr et al used Kinect to obtain distance information and realized 3D reconstruction of scene by classifying the statistical thick and thin areas, but the TOF sensor was limited in resolution [9]. Yang et al proposed a 3D reconstruction system combining binocular and TOF depth cameras to accurately identify the distance between objects and the camera, promote the scene acquisition resolution and stereo matching effect, and improve the reconstruction accuracy, however, the system adopts an active sensing method with large scale and high power consumption [10]. Compared with binocular and RGB-D camera, monocular camera has high adaptability and low power consumption.…”

Section: ⅱ Related Workmentioning

confidence: 99%

Scene Target 3D Point Cloud Reconstruction Technology Combining Monocular Focus Stack and Deep Learning

Hu¹,

Wang²,

Wang³

2020

IEEE Access

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In order to obtain the depth information of the target in the scene and realize threedimensional (3D) reconstruction, in this paper, a target reconstruction method combining monocular focus stack image and deep neural network is proposed. This method makes full use of the advantages of light field imaging technology and can generate the all focused image. The method first collects multiple frames of continuous images at different focal lengths of the scene, using a divide and conquer algorithm strategy, uplink uses YOLO neural network to identify the target in 3D space and track the position information; the downlink reconstructs the four-dimensional (4D) light field data based on the focus stack image frequency domain back projection, and then uses light field imaging technology to invert the scene parallax; subsequently, achieve scene depth estimation and reconstruction of all focused image; finally, the uplink and downlink are merged to realize the reconstruction of the 3D point cloud of the space target. Experimental results on real scenes show the effectiveness of the proposed algorithm. INDEX TERMSFocus stack image, Deep learning, Light field reconstruction, All focus image, 3D reconstruction.

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Vision System of Mobile Robot Combining Binocular and Depth Cameras

Cited by 13 publications

References 21 publications

A Review on Challenges of Autonomous Mobile Robot and Sensor Fusion Methods

A Review on Challenges of Autonomous Mobile Robot and Sensor Fusion Methods

Binocular Vision-Based Intelligent 3-D Perception for Robotics Application

Scene Target 3D Point Cloud Reconstruction Technology Combining Monocular Focus Stack and Deep Learning

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