Repair technologies have been considered as sustainable approaches due to their capability to restore value in a damaged component and bring it to like-new condition. However, in contrast to a manufacturing process benefiting from an automated environment, the automation level for repair and remanufacturing processes remains low. With the aim of moving the repair industry towards autonomy, this study proposes a novel repair framework. The developed methodology presents a vision-based Robotic Laser Cladding Repair Cell (RLCRC) that has two features: (a) an intelligent inspection system that uses a deep learning model to automatically detect the damaged region in an image; (b) employing computer vision-based calibration and 3D scanning techniques to precisely identify the geometries of damaged area. The repair of fixed bends is selected as the case study. The results obtained validate the efficacy of the proposed framework, enabling automatic damage detection and damaged volume extraction for worn fixed bends. Following the suggested framework, a time reduction of more than 63% is reported. 

Vision-based spatial damage localization method for autonomous robotic laser cladding repair processes

Outlier-robust estimation is a fundamental problem and has been extensively investigated by statisticians and practitioners. The last few years have seen a convergence across research fields towards"algorithmic robust statistics", which focuses on developing tractable outlier-robust techniques for high-dimensional estimation problems. Despite this convergence, research efforts across fields have been mostly disconnected from one another. This monograph bridges recent work on certifiable outlier-robust estimation for geometric perception in robotics and computer vision with parallel work in robust statistics. In particular, we adapt and extend recent results on robust linear regression (applicable to the low-outlier regime with<<50% outliers) and list-decodable regression (applicable to the high-outlier regime with>>50% outliers) to the setup commonly found in robotics and vision, where (i) variables (e.g., rotations, poses) belong to a non-convex domain, (ii) measurements are vector-valued, and (iii) the number of outliers is not known a priori. The emphasis here is on performance guarantees: rather than proposing radically new algorithms, we provide conditions on the input measurements under which modern estimation algorithms (possibly after small modifications) are guaranteed to recover an estimate close to the ground truth in the presence of outliers. These conditions are what we call an"estimation contract". Besides the proposed extensions of existing results, we believe the main contributions of this monograph are (i) to unify parallel research lines by pointing out commonalities and differences, (ii) to introduce advanced material (e.g., sum-of-squares proofs) in an accessible and self-contained presentation for the practitioner, and (iii) to point out a few immediate opportunities and open questions in outlier-robust geometric perception.

Estimation Contracts for Outlier-Robust Geometric Perception

Assessing the impact of 3D point neighborhood size selection on unsupervised spall classification with 3D bridge point clouds

Accurate, self-consistent bathymetric maps are needed to monitor changes in subsea environments and infrastructure. These maps are increasingly collected by underwater vehicles, and mapping requires an accurate vehicle navigation solution. Commercial off-the-shelf (COTS) navigation solutions for underwater vehicles often rely on external acoustic sensors for localization; however, survey-grade acoustic sensors are expensive to deploy and limit the range of the vehicle. Techniques from the field of simultaneous localization and mapping, particularly loop closures, can improve the quality of the navigation solution over dead reckoning, but are difficult to integrate into COTS navigation systems. This article presents a method to improve the self-consistency of bathymetric maps by smoothly integrating loop-closure measurements into the state estimate produced by a commercial subsea navigation system. Integration is done using a white-noise-on-acceleration motion prior, without access to raw sensor measurements or proprietary models. Improvements in map self-consistency are shown for both simulated and experimental datasets, including a 3-D scan of an underwater shipwreck in Wiarton, ON, Canada. 

/pdf/improving-self-consistency-in-underwater-mapping-through-1sdjkdf8.pdf

Improving Self-Consistency in Underwater Mapping Through Laser-Based Loop Closure

We present a novel method for 3D shape representation learning using multi-scale wavelet decomposition. Previous works often decompose 3D shapes into complementary components in spatial domain at a single scale. In this work, we study to decompose 3D shapes into sub-bands components in frequency domain at multiple scales, resulting in a hierarchical decomposition tree in a principled manner rooted in multi-resolution wavelet analysis . Specifically, we propose Adaptive Wavelet Transformer Network (AWT-Net) that firstly generates approximation or detail wavelet coefficients per point, classifying each point into high or low sub-bands components, using lifting scheme at multiple scales recursively and hierarchically. Then, AWT-Net exploits Transformer to enhance the original shape features by querying and fusing features from different but integrated sub-bands. The wavelet coefficients can be learned without direct supervision on coefficients, and AWT-Net is fully differentiable and can be learned in an end-to-end fashion. Extensive experiments demonstrate that AWT-Net achieves competitive performance on 3D shape classification and segmentation benchmarks.

Adaptive Wavelet Transformer Network for 3D Shape Representation Learning

Point cloud registration is a means of achieving loop closure correction within a simultaneous localization and mapping (SLAM) algorithm. Data association is a critical component in point cloud registration, and can be very challenging in feature-depleted environments such as seabed. This paper presents a point cloud registration pipeline for performing loop closure correction in feature-depleted subsea environments using data collected from an optical scanner. The pipeline uses Gaussian process regression to extract keypoint sets, and a weighted network alignment algorithm to propose point correspondences. A variant of the iterative closest point (ICP) registration algorithm is used to perform fine alignment, with point correspondences informed by the mappings determined following the network alignment step. The developed registration pipeline is deployed with success on a challenging section of field data containing topography that cannot be resolved using conventional imaging sonar.

A Point Cloud Registration Pipeline using Gaussian Process Regression for Bathymetric SLAM

Random walks for unorganized point cloud segmentation with application to aerospace repair

Scan matching is an established method of identifying loop closure in bathymetric SLAM. Newly developed subsea optical scanners offer much greater resolution compared to imaging sonar, but have a limited field of view. This naturally leads to higher outlier rates when performing scan registration, decreasing the success rate of traditional iterative closest point (ICP) algorithms. This work evaluates the effectiveness of different robust cost functions for outlier rejection on field data collected using an AUV-mounted optical scanner.

Comparing Robust Cost Functions for Bathymetric Point Cloud Registration

Accurate, self-consistent bathymetric maps are needed to monitor changes in subsea environments and infrastructure. These maps are increasingly collected by underwater vehicles, and mapping requires an accurate vehicle navigation solution. Commercial off-the-shelf (COTS) navigation solutions for underwater vehicles often rely on external acoustic sensors for localization; however, survey-grade acoustic sensors are expensive to deploy and limit the range of the vehicle. Techniques from the field of simultaneous localization and mapping, particularly loop closures, can improve the quality of the navigation solution over dead reckoning, but are difficult to integrate into COTS navigation systems. This article presents a method to improve the self-consistency of bathymetric maps by smoothly integrating loop-closure measurements into the state estimate produced by a commercial subsea navigation system. Integration is done using a white-noise-on-acceleration motion prior, without access to raw sensor measurements or proprietary models. Improvements in map self-consistency are shown for both simulated and experimental datasets, including a 3-D scan of an underwater shipwreck in Wiarton, ON, Canada.

Measurement outliers are unavoidable when solving real-world robot state estimation problems. A large family of robust loss functions (RLFs) exists to mitigate the effects of outliers, including newly developed adaptive methods that do not require parameter tuning. All of these methods assume that residuals follow a zero-mean Gaussian-like distribution. However, in multivariate problems the residual is often defined as a norm, and norms follow a Chi-like distribution with a non-zero mode value. This produces a “mode gap” that impacts the convergence rate and accuracy of existing RLFs. The proposed approach, “Adaptive MB,” accounts for this gap by first estimating the mode of the residuals using an adaptive Chi-like distribution. Applying an existing adaptive weighting scheme only to residuals greater than the mode leads to more robust performance and faster convergence times in two fundamental state estimation problems, point cloud alignment and pose averaging.

Mind the Gap: Norm-Aware Adaptive Robust Loss for Multivariate Least-Squares Problems

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