RGB-DI Images and Full Convolution Neural Network-Based Outdoor Scene Understanding for Mobile Robots

Qiu, Zengshuai; Zhuang, Yan; Yan, Fei; Hu, Huosheng; Wang, Wei

doi:10.1109/tim.2018.2834085

Cited by 51 publications

(13 citation statements)

References 23 publications

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“…Furthermore, a multisensor-based approach using vision and laser sensors has been proposed in ref. [164] to generate features of the image such as RGB, depth and intensity (RGB-DI). A fully convolution network (FCN) with deep layers was designed to perform semantic segmentation of RGB-DI images.…”

Section: Deep Learningmentioning

confidence: 99%

Comprehensive Review on Reaching and Grasping of Objects in Robotics

Mohammed¹,

Chua²,

Kwek³

2021

Robotica

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SUMMARY Interaction between a robot and its environment requires perception about the environment, which helps the robot in making a clear decision about the object type and its location. After that, the end effector will be brought to the object’s location for grasping. There are many research studies on the reaching and grasping of objects using different techniques and mechanisms for increasing accuracy and robustness during grasping and reaching tasks. Thus, this paper presents an extensive review of research directions and topics of different approaches such as sensing, learning and gripping, which have been implemented within the current five years.

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Section: Deep Learningmentioning

confidence: 99%

Comprehensive Review on Reaching and Grasping of Objects in Robotics

Mohammed¹,

Chua²,

Kwek³

2021

Robotica

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“…Another way to represent point cloud data in a 2D format is setting a virtual camera for collecting images from the scene. In addition to the RGB color and the depth that can be projected to the image, geometric features (e.g., normals, incidence angels) and radiometric information (e.g., intensity) can also be fed to the deep learning architecture (e.g., Zhuang, et al [ 171 ], Lawin, et al [ 172 ], Qiu, et al [ 173 ]). Instead of converting 3D data to 2D images, some other methods voxelize the point cloud data and develop deep learning techniques that can cope with voxels (e.g., Huang and You [ 174 ]).…”

Section: Classificationmentioning

confidence: 99%

Object Recognition, Segmentation, and Classification of Mobile Laser Scanning Point Clouds: A State of the Art Review

Che

Jung

2019

Sensors

198

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Mobile Laser Scanning (MLS) is a versatile remote sensing technology based on Light Detection and Ranging (lidar) technology that has been utilized for a wide range of applications. Several previous reviews focused on applications or characteristics of these systems exist in the literature, however, reviews of the many innovative data processing strategies described in the literature have not been conducted in sufficient depth. To this end, we review and summarize the state of the art for MLS data processing approaches, including feature extraction, segmentation, object recognition, and classification. In this review, we first discuss the impact of the scene type to the development of an MLS data processing method. Then, where appropriate, we describe relevant generalized algorithms for feature extraction and segmentation that are applicable to and implemented in many processing approaches. The methods for object recognition and point cloud classification are further reviewed including both the general concepts as well as technical details. In addition, available benchmark datasets for object recognition and classification are summarized. Further, the current limitations and challenges that a significant portion of point cloud processing techniques face are discussed. This review concludes with our future outlook of the trends and opportunities of MLS data processing algorithms and applications.

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“…In recent years, many methods of deep learning have been applied in practice. Qiu et al (2018) presented a multisensor-based approach to outdoor scene understanding of mobile robots, which uses fully convolutional neural network (FCN) to perform semantic segmentation for RGB, depth and intensity (RGB-DI) images. Levine et al (2016) proposed a method involving hand-eye coordination for robotic grasping from monocular images, which trained a large convolutional neural network to predict the probability of successful grasps by task-space motion of the gripper, using only monocular camera images and independently of camera calibration or the current robot pose.…”

Section: Introductionmentioning

confidence: 99%

A fast detection and grasping method for mobile manipulator based on improved faster R-CNN

Zhang

Tan

Zhao

et al. 2020

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Purpose This paper aims to solve the problem between detection efficiency and performance in grasp commodities rapidly. A fast detection and grasping method based on improved faster R-CNN is purposed and applied to the mobile manipulator to grab commodities on the shelf. Design/methodology/approach To reduce the time cost of algorithm, a new structure of neural network based on faster R CNN is designed. To select the anchor box reasonably according to the data set, the data set-adaptive algorithm for choosing anchor box is presented; multiple models of ten types of daily objects are trained for the validation of the improved faster R-CNN. The proposed algorithm is deployed to the self-developed mobile manipulator, and three experiments are designed to evaluate the proposed method. Findings The result indicates that the proposed method is successfully performed on the mobile manipulator; it not only accomplishes the detection effectively but also grasps the objects on the shelf successfully. Originality/value The proposed method can improve the efficiency of faster R-CNN, maintain excellent performance, meet the requirement of real-time detection, and the self-developed mobile manipulator can accomplish the task of grasping objects.

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RGB-DI Images and Full Convolution Neural Network-Based Outdoor Scene Understanding for Mobile Robots

Cited by 51 publications

References 23 publications

Comprehensive Review on Reaching and Grasping of Objects in Robotics

Comprehensive Review on Reaching and Grasping of Objects in Robotics

Object Recognition, Segmentation, and Classification of Mobile Laser Scanning Point Clouds: A State of the Art Review

A fast detection and grasping method for mobile manipulator based on improved faster R-CNN

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