Prior-knowledge assisted fast 3D map building of structured environments for steel bridge maintenance

Sehestedt, Stephan; Paul, Gavin; Rushton-Smith, David; Liu, Dikai

doi:10.1109/coase.2013.6653892

Cited by 7 publications

(5 citation statements)

References 23 publications

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“…Minimizing the number of viewpoints using Art Gallery Problem (AGP) is another approach to compute the optimal path [27,28,29]. Coverage path planning has been mostly applied on 3D-mapping [30,31], surveying of urban environments [32,33,34], ship hull inspection [28,35,36], and structural inspection [37]. The model of Bircher et al [37] which is based on Lin-Kernighan Heuristic (LKH) as a TSP solver, convex optimization, and Rapidly-exploring Random Tree star (RRT*) algorithm to define the optimized viewpoints and path, can be applied for other types of structures.…”

Section: Uav Path Planningmentioning

confidence: 99%

High Level Framework for Bridge Inspection Using LiDAR-Equipped UAV

Bolourian¹,

Soltani²,

Hammad³

et al. 2017

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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-According to Statistics Canada, bridges in Canada have a service life of approximately 43 years. With the majority of bridges passing half of their expected service life, a large amount of investment needs to be made to inspect and maintain them in a safe condition. Manual inspection methods are both time-consuming and costly, which may discourage further inspections and follow-up of defects. Thus, there is a great need for using an automated data collection system. Surface defects (e.g. cracks) in concrete bridges can be inspected using 3D Light Detection and Ranging (LiDAR) scanner as a Non-Destructive Testing (NDT) method. However, the commonly used terrestrial LiDAR is limited to stationary data collection, which reduces the accessibility to some components of the bridges. To tackle this limitation, a LiDAR attached to an Unmanned Aerial Vehicle (UAV) provides more flexibility and accessibility for inspecting large surface areas without threatening inspectors' safety. After providing a comprehensive literature review about the usage of UAVs and LiDAR for the inspection of different types of structures, this paper proposes a high level framework for bridge inspection using LiDAR-equipped UAV. The framework includes the following steps: (1) planning a collisionfree optimized path with respect to the minimum cost and maximum coverage considering a variety of constrains and requirements related to the hardware and the inspection task, and (2) data analysis for detecting the surface defects based on the collected point clouds.

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Section: Uav Path Planningmentioning

confidence: 99%

High Level Framework for Bridge Inspection Using LiDAR-Equipped UAV

Bolourian¹,

Soltani²,

Hammad³

et al. 2017

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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“…A different approach for 3D mapping was presented in the study by Sehestedt et al 43 utilizing a depth sensor mounted on a grit-blasting assistive device consisting of a robotic manipulator and a Xion RGBD camera. The mapping was preformed by starting with a workspace fundamental element, such as a roof point cloud, to identify other regions and narrowed down until the map matches a template, thus enhancing the geometrical accuracy of the map.…”

Section: (Rmap)mentioning

confidence: 99%

A survey on inspecting structures using robotic systems

Almadhoun

Taha

Seneviratne

et al. 2016

International Journal of Advanced Robotic Systems

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Advancements in robotics and autonomous systems are being deployed nowadays in many application domains such as search and rescue, industrial automation, domestic services and healthcare. These systems are developed to tackle tasks in some of the most challenging, labour intensive and dangerous environments. Inspecting structures (e.g. bridges, buildings, ships, wind turbines and aircrafts) is considered a hard task for humans to perform and of critical importance since missing any details could affect the structure's performance and integrity. Additionally, structure inspection is time and resource intensive and should be performed as efficiently and accurately as possible. Inspecting various structures has been reported in the literature using different robotic platforms to: inspect difficult to reach areas and detect various types of faults and anomalies. Typically, inspection missions involve performing three main tasks: coverage path planning, shape, model or surface reconstruction and the actual inspection of the structure. Coverage path planning ensures the generation of an optimized path that guarantees the complete coverage of the structure of interest in order to gather highly accurate information to be used for shape/model reconstruction. This article aims to provide an overview of the recent work and breakthroughs in the field of coverage path planning and model reconstruction, with focus on 3D reconstruction, for the purpose of robotic inspection.

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“…Most engineering approaches greedily select sensing viewpoints that are predicted to significantly reduce the uncertainty within the map [6]- [8]. Alternatively, approaches may adopt optimal exploration schemes based upon locating structural features about an environment [5], [8], so as to fit templates that encapsulate prior knowledge to the data [9].…”

Section: Introductionmentioning

confidence: 99%

A novel approach to steel rivet detection in poorly illuminated steel structural environments

Paul

Liu

Dikai

2016

2016 14th International Conference on Control, Automation, Robotics and Vision (ICARCV)

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It is becoming increasingly achievable for steel bridge structures, which are normally both inaccessible and hazardous for humans, to be inspected and maintained by autonomous robots. Steel bridges have been traditionally constructed by securing plate members together with rivets. However, rivets present a challenge for robots both in terms of cleaning and surface traversal. This paper presents a novel approach to RGB-D image and point cloud analysis that enables rivets to be rapidly and robustly located using low cost, non-contact sensing devices that can be easily affixed to a robot. The approach performs classification based on: (a) high-intensity blobs in color images, (b) the non-linear perturbations in depth images, and (c) surface normal clusters in 3D point clouds. The predicted rivet locations from the three classifiers are combined using a probabilistic occupancy mapping technique. Experiments are conducted in several different lab and real-world steel bridge environments, where there is no external lighting infrastructure, and the sensors are attached to a mobile platform, i.e. a climbing inspection robot. The location of rivets within 2m of the robot can be robustly located within 10mm of their correct location. The state of voxels can be predicted with above 95% accuracy, in approximately 1 second per frame.

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Prior-knowledge assisted fast 3D map building of structured environments for steel bridge maintenance

Cited by 7 publications

References 23 publications

High Level Framework for Bridge Inspection Using LiDAR-Equipped UAV

High Level Framework for Bridge Inspection Using LiDAR-Equipped UAV

A survey on inspecting structures using robotic systems

A novel approach to steel rivet detection in poorly illuminated steel structural environments

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