The PASCAL Visual Object Classes Challenge

Learning Deconvolution Network for Semantic Segmentation

Recently, unmanned aerial vehicles (UAVs), or drones, have attracted a lot of attention, since they represent a new potential market. Along with the maturity of the technology and relevant regulations, a worldwide deployment of these UAVs is expected. Thanks to the high mobility of drones, they can be used to provide a lot of applications, such as service delivery, pollution mitigation, farming, and in the rescue operations. Due to its ubiquitous usability, the UAV will play an important role in the Internet of Things (IoT) vision, and it may become the main key enabler of this vision. While these UAVs would be deployed for specific objectives (e.g., service delivery), they can be, at the same time, used to offer new IoT value-added services when they are equipped with suitable and remotely controllable machine type communications (MTCs) devices (i.e., sensors, cameras, and actuators). However, deploying UAVs for the envisioned purposes cannot be done before overcoming the relevant challenging issues. These challenges comprise not only technical issues, such as physical collision, but also regulation issues as this nascent technology could be associated with problems like breaking the privacy of people or even use it for illegal operations like drug smuggling. Providing the communication to UAVs is another challenging issue facing the deployment of this technology. In this paper, a comprehensive survey on the UAVs and the related issues will be introduced. In addition, our envisioned UAV-based architecture for the delivery of UAV-based value-added IoT services from the sky will be introduced, and the relevant key challenges and requirements will be presented.

Low-Altitude Unmanned Aerial Vehicles-Based Internet of Things Services: Comprehensive Survey and Future Perspectives

Unmanned aerial vehicles (UAVs), or aerial drones, are an emerging technology with significant market potential. UAVs may lead to substantial cost savings in, for instance, monitoring of difficult-to-access infrastructure, spraying fields and performing surveillance in precision agriculture, as well as in deliveries of packages. In some applications, like disaster management, transport of medical supplies, or environmental monitoring, aerial drones may even help save lives. In this article, we provide a literature survey on optimization approaches to civil applications of UAVs. Our goal is to provide a fast point of entry into the topic for interested researchers and operations planning specialists. We describe the most promising aerial drone applications and outline characteristics of aerial drones relevant to operations planning. In this review of more than 200 articles, we provide insights into widespread and emerging modeling approaches. We conclude by suggesting promising directions for future research.

Optimization approaches for civil applications of unmanned aerial vehicles (UAVs) or aerial drones: A survey

A review: On path planning strategies for navigation of mobile robot

Robot 3D three-dimension path planning targets for finding an optimal and collision-free path in a 3D workspace while taking into account kinematic constraints including geometric, physical, and temporal constraints. The purpose of path planning, unlike motion planning which must be taken into consideration of dynamics, is to find a kinematically optimal path with the least time as well as model the environment completely. We discuss the fundamentals of these most successful robot 3D path planning algorithms which have been developed in recent years and concentrate on universally applicable algorithms which can be implemented in aerial robots, ground robots, and underwater robots. This paper classifies all the methods into five categories based on their exploring mechanisms and proposes a category, called multifusion based algorithms. For all these algorithms, they are analyzed from a time efficiency and implementable area perspective. Furthermore a comprehensive applicable analysis for each kind of method is presented after considering their merits and weaknesses.

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Survey of Robot 3D Path Planning Algorithms

3D path planning of unmanned aerial vehicle (UAV) targets at finding an optimal and collision free path in a 3D cluttered environment while taking into account the geometric, physical and temporal constraints. Although a lot of works have been done to solve UAV 3D path planning problem, there lacks a comprehensive survey on this topic, let alone the recently published works that focus on this field. This paper analyses the most successful UAV 3D path planning algorithms that developed in recent years. This paper classifies the UAV 3D path planning methods into five categories, sampling-based algorithms, node-based algorithms, mathematical model based algorithms, Bio-inspired algorithms, and multi-fusion based algorithms. For each category a critical analysis and comparison is given. Furthermore a comprehensive applicable analysis for each kind of method is presented after considering its working mechanism and time complexity.

A literature review of UAV 3D path planning

The impact-echo (IE) acoustic inspection method is a non-destructive evaluation technique, which has been widely applied to detect the defects, structural deterioration level, and thickness of plate-like concrete structures. This paper presents a novel climbing robot, namely Rise-Rover, to perform automated IE signal collection from concrete structures with IE signal analyzing based on machine learning techniques. Rise-Rover is our new generation robot, and it has a novel and enhanced absorption system to support heavy load, and crawler-like suction cups to maintain high mobility performance while crossing small grooves. Moreover, the design enables a seamless transition between ground and wall. This paper applies the fast Fourier transform and wavelet transform for feature detection from collected IE signals. A distance metric learning based support vector machine approach is newly proposed to automatically classify the IE signals. With the visual-inertial odometry of the robot, the detected flaws of inspection area on the concrete plates are visualized in 2D/3D. Field tests on a concrete bridge deck demonstrate the efficiency of the proposed robot system in automatic health condition assessment for concrete structures.

Wall-climbing robot for non-destructive evaluation using impact-echo and metric learning SVM

The concrete aging problem has gained more attention in recent years as more bridges and tunnels in the United States lack proper maintenance. Though the Federal Highway Administration requires these public concrete structures to be inspected regularly, on-site manual inspection by human operators is time-consuming and labor-intensive. Conventional inspection approaches for concrete inspection, using RGB image-based thresholding methods, are not able to determine metric information as well as accurate location information for assessed defects for conditions. To address this challenge, we propose a deep neural network &#x0028 DNN &#x0029 based concrete inspection system using a quadrotor flying robot &#x0028 referred to as CityFlyer &#x0029 mounted with an RGB-D camera. The inspection system introduces several novel modules. Firstly, a visual-inertial fusion approach is introduced to perform camera and robot positioning and structure 3D metric reconstruction. The reconstructed map is used to retrieve the location and metric information of the defects. Secondly, we introduce a DNN model, namely AdaNet, to detect concrete spalling and cracking, with the capability of maintaining robustness under various distances between the camera and concrete surface. In order to train the model, we craft a new dataset, i.e., the concrete structure spalling and cracking &#x0028 CSSC &#x0029 dataset, which is released publicly to the research community. Finally, we introduce a 3D semantic mapping method using the annotated framework to reconstruct the concrete structure for visualization. We performed comparative studies and demonstrated that our AdaNet can achieve 8.41 &#x0025 higher detection accuracy than ResNets and VGGs. Moreover, we conducted five field tests, of which three are manual hand-held tests and two are drone-based field tests. These results indicate that our system is capable of performing metric field inspection, and can serve as an effective tool for civil engineers.

Concrete defects inspection and 3D mapping using CityFlyer quadrotor robot

In this paper, we exploit the concrete surface flaw inspection through the fusion of visual positioning and semantic segmentation approach. The fused inspection result is represented by a 3D metric map with a spatial area, width, and depth information, which shows the advantage over general inspection in image space without metric info. We also relieve the human labor with an automatic labeling approach. The system is composed of three hybrid parts: visual positioning to enable pose association, crack/spalling inspection using a deep neural network (pixel level), and a 3D random field filter for fusion to achieve a global 3D metric map. To improve the infrastructure inspection, we released a new data set for concrete crack and spalling segmentation which is built on CSSC dataset [27]. To leverage the effectiveness of the large-scale SLAM aided semantic inspection, we performed three field tests and one baseline test. Experimental results show that our proposed approach significantly improves the capability of 3D metric concrete inspection via deploying visual SLAM. Furthermore, we achieve an 82.4% MaxF1 score for crack detection and 88.64% MaxF1 score for spalling detection on the relabeled dataset.

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Liang Yang

Papers

Survey of Robot 3D Path Planning Algorithms

A literature review of UAV 3D path planning

Wall-climbing robot for non-destructive evaluation using impact-echo and metric learning SVM

Concrete defects inspection and 3D mapping using CityFlyer quadrotor robot

Semantic Metric 3D Reconstruction for Concrete Inspection