Image-based 3-D reconstruction of constrained environments

Nuclear power plays a important role to the United Kingdom electricity generation infrastructure, providing a reliable baseload of low carbon electricity. The Advanced Gas-cooled Reactor (AGR) design makes up approximately 50% of the existing fleet, however, many of the operating reactors have exceeding their original design lifetimes.To ensure safe reactor operation, engineers perform periodic in-core visual inspections of reactor components to monitor the structural health of the core as it ages. However, current inspection mechanisms deployed provide limited structural information about the fuel channel or defects.;This thesis investigates the suitability of image-based 3-D reconstruction techniques to acquire 3-D structural geometry to enable improved diagnostic and prognostic abilities for inspection engineers. The application of image-based 3-D reconstruction to in-core inspection footage highlights significant challenges, most predominantly that the image saliency proves insuffcient for general reconstruction frameworks. The contribution of the thesis is threefold. Firstly, a novel semi-dense matching scheme which exploits sparse and dense image correspondence in combination with a novel intra-image region strength approach to improve the stability of the correspondence between images.;This results in a percentage increase of 138.53% of correct feature matches over similar state-of-the-art image matching paradigms. Secondly, a bespoke incremental Structure-from-Motion (SfM) framework called the Constrained Homogeneous SfM (CH-SfM) which is able to derive structure from deficient feature spaces and constrained environments. Thirdly, the application of the CH-SfM framework to remote visual inspection footage gathered within AGR fuel channels, outperforming other state-of-the-art reconstruction approaches and extracting representative 3-D structural geometry of orientational scans and fully circumferential reconstructions.;This is demonstrated on in-core and laboratory footage, achieving an approximate 3-D point density of 2.785 - 23.8025NX/cm² for real in-core inspection footage and high quality laboratory footage respectively. The demonstrated novelties have applicability to other constrained or feature-poor environments, with future work looking to producing fully dense, photo-realistic 3-D reconstructions.Nuclear power plays a important role to the United Kingdom electricity generation infrastructure, providing a reliable baseload of low carbon electricity. The Advanced Gas-cooled Reactor (AGR) design makes up approximately 50% of the existing fleet, however, many of the operating reactors have exceeding their original design lifetimes.To ensure safe reactor operation, engineers perform periodic in-core visual inspections of reactor components to monitor the structural health of the core as it ages. However, current inspection mechanisms deployed provide limited structural information about the fuel channel or defects.;This thesis investigates the suitability of image-based 3-D reconstruction techniques to acquire 3-D structural geometry to enable improved diagnostic and prognostic abilities for inspection engineers. The application of image-based 3-D reconstruction to in-core inspection footage highlights significant challenges, most predominantly that the image saliency proves insuffcient for general reconstruction frameworks. The contribution of the thesis is threefold. Firstly, a novel semi-dense matching scheme which exploits sparse and dense image correspondence in combination with a novel intra-image region strength approach to improve the stability of the correspondence between images.;This results in a percentage increase of 138.53% of correct feature matches over similar state-of-the-art image matching paradigms. Secondly, a bespoke incremental Structure-from-Motion (SfM) framework called the Constrained Homogeneous SfM (CH-SfM) which is able to derive structure from deficient feature spaces and constrained environments. Thirdly, the application of the CH-SfM framework to remote visual inspection footage gathered within AGR fuel channels, outperforming other state-of-the-art reconstruction approaches and extracting representative 3-D structural geometry of orientational scans and fully circumferential reconstructions.;This is demonstrated on in-core and laboratory footage, achieving an approximate 3-D point density of 2.785 - 23.8025NX/cm² for real in-core inspection footage and high quality laboratory footage respectively. The demonstrated novelties have applicability to other constrained or feature-poor environments, with future work looking to producing fully dense, photo-realistic 3-D reconstructions

Towards extracting 3-D structural representations of AGR core fuel channels from 2-D in-core inspection videos

Remote Visual Inspection (RVI) of Advanced Gas-cooled Reactor (AGR) nuclear power stations allows engineers to gain an understanding of the AGR graphite core health by investigating the incorporated fuel channels. During planned, periodic outages, video footage of the pre-selected fuel channels is acquired using specialist inspection tools and is subsequently taken offline for further analysis using visualization techniques. Current methods of visualization however provide limited structural information due to the loss of depth information as a direct result of the image acquisition process. This paper introduces a new bespoke 3-D reconstruction framework to recover lost depth information to produce 3-D point cloud reconstructions of fuel channels from inspection videos. We also present here a new, lab based, experimental rig setup with which we effectively captured data under lab controlled conditions to verify our 3-D reconstruction algorithms. Our proposed method is tested on 2-D in core inspection videos in addition to the footage captured within laboratory conditions and outperforms state-of-the-art incremental reconstruction frameworks whilst producing a more representative 3-D point cloud for improved in-core visualization

3-D advanced gas-cooled nuclear reactor fuel channel reconstruction using structure-from-motion

During planned, periodic outages, a selection of fuel channels within the UK fleet of Advanced Gas-cooled Reactor (AGR) cores are inspected using specialist tools which record video footage and other sensory data for each channel which undergoes inspection. Current visualization techniques comprise of manually produced montages by inspection engineers of points of interest (i.e. structural defects) and 2-D panoramic images of the fuel channels automatically produced using bespoke image stitching software. Both techniques however provide limited structural information due to the loss of depth data as a result of the image formation process. By recovering the depth information from the footage, a 3-D model could be constructed and subsequently, allow for more accurate profiling of specific defects observed during inspection in addition to obtaining the fuel channels structure using existing footage. This work explores the preliminary application of a 3-D visualization technique known as Structure-from-Motion (SfM) which aims to obtain 3-D information by exploiting image correspondences across multiple viewpoints of the same scene in the RVI footage. This paper investigates the difficulties of applying state-of-the-art SfM to RVI footage and we present new techniques to improve feature correspondence searching in repetitive, non-descript environments

Automated video processing and image analysis software to support visual inspection of AGR cores

Remote visual inspection of fuel channels in Advanced Gas-cooled Reactor (AGR) cores provides nuclear operators with an understanding of the condition of the UK’s fleet of nuclear power plants. During planned, periodic outages, specialist inspection tools equipped with video cameras and other sensors are manipulated inside fuel channels selected for inspection and a video of the entire channel bore is recorded for each. If cracks are observed in this process, a montage of the entire crack region needs to be: produced, analysed and sentenced (classifying the crack morphology, location, orientation and size) before the station is returned to service – provided it is safe to do so. At the present time, the video analysis and crack montage production is done manually by an expert team of inspection engineers. In line with this process, bespoke image stitching software named “ASIST” (Automated Software Image Stitching Tool) has been trialled in the last 12 months and evaluated using data from: Dungeness, Hunterston B, Hinkley Point B, Heysham 1 and Torness outages. The software is now almost ready to replace the manual process and will provide higher quality images with 100% channel visualisation properties in a fraction of the time taken by the current approach. This paper provides a summary of the ASIST evaluation undertaken in the last year. It also describes recent research endeavours aiming to provide ASIST with: crack detection techniques; keyway locating algorithms and methods to compute Structure-from-Motion which will facilitate the extraction of 3D depth information directly from the 2D video footage

3-D visualization of AGR fuel channel bricks using Structure-from-Motion

This paper outlines a new framework for applying Structure-from-Motion (SfM) to challenging, feature-poor environments such as those observed during AGR fuel channel inspection. Deriving structural information from AGR inspection footage is challenging due to several key issues; lack of discriminative salient features within the channel, inconsistency in lighting during the inspection process, lack of textural information within the channel and noise from the inspection equipment. This presents difficulties to techniques such as SfM due to its reliance on finding and reliably tracking a set of robust features from multiple viewpoints. This paper introduces the first use of an incremental 3-D reconstruction framework which can produce reconstructions of footage obtained within a nuclear reactor. It approaches this issue by introducing a novel correspondence searching methodology which can operate within feature-poor environments by utilising a constrained, iterative threshold matching technique to obtain robust feature matches. This paper demonstrates the approach using two datasets; laboratory footage obtained from an experimental setup emulating a small sub-section of the channel and in-core inspection footage of AGR fuel channels

3-D reconstruction of AGR fuel channels using RVI footage

Within the U.K, there is a fleet of 7 Advanced Gas-cooled Reactor (AGR) stations in operation which are close to or have already exceeded their original estimated design lifetime. As the reactors continue to age, it is critical to gain an understanding of the structural morphology of the AGR fuel channels and to quantify changes in the physical properties of the graphite bricks which occur during reactor operation. To assess such operational changes, periodic Remote Visual Inspection (RVI) is carried out on pre-selected channels using specialist inspection tools which gather video and other sensory data of the channel bore. Current visualisation of the video provides limited 3-D structural information due to the image acquisition process which results in a loss of depth information. This paper describes a novel image reconstruction framework based on a technique called Structure-from-Motion (SfM) which has been designed to ascertain 3-D structural information directly from 2-D in-core RVI data. This paper also discusses the challenges of applying 3-D reconstruction techniques like SfM to pre-existing inspection data before demonstrating that the proposed framework can be applied to data captured under laboratory conditions and to subsets of real in-core inspection videos to generate singular and circumferential point cloud reconstructions of the channel bore