Semi-Global Stereo Matching with Surface Orientation Priors

Semi-Global Matching (SGM) is a widely-used efficient stereo matching technique. It works well for textured scenes, but fails on untextured slanted surfaces due to its fronto-parallel smoothness assumption. To remedy this problem, we propose a simple extension, termed SGM-P, to utilize precomputed surface orientation priors. Such priors favor different surface slants in different 2D image regions or 3D scene regions and can be derived in various ways. In this paper we evaluate plane orientation priors derived from stereo matching at a coarser resolution and show that such priors can yield significant performance gains for difficult weakly-textured scenes. We also explore surface normal priors derived from Manhattan-world assumptions, and we analyze the potential performance gains using oracle priors derived from ground-truth data. SGM-P only adds a minor computational overhead to SGM and is an attractive alternative to more complex methods employing higher-order smoothness terms.Comment: extended draft of 3DV 2017 (spotlight) pape

A Convex Polynomial Force-Motion Model for Planar Sliding: Identification and Application

We propose a polynomial force-motion model for planar sliding. The set of generalized friction loads is the 1-sublevel set of a polynomial whose gradient directions correspond to generalized velocities. Additionally, the polynomial is confined to be convex even-degree homogeneous in order to obey the maximum work inequality, symmetry, shape invariance in scale, and fast invertibility. We present a simple and statistically-efficient model identification procedure using a sum-of-squares convex relaxation. Simulation and robotic experiments validate the accuracy and efficiency of our approach. We also show practical applications of our model including stable pushing of objects and free sliding dynamic simulations.Comment: 2016 IEEE International Conference on Robotics and Automation (ICRA

Bayesian nonparametric multivariate convex regression

In many applications, such as economics, operations research and reinforcement learning, one often needs to estimate a multivariate regression function f subject to a convexity constraint. For example, in sequential decision processes the value of a state under optimal subsequent decisions may be known to be convex or concave. We propose a new Bayesian nonparametric multivariate approach based on characterizing the unknown regression function as the max of a random collection of unknown hyperplanes. This specification induces a prior with large support in a Kullback-Leibler sense on the space of convex functions, while also leading to strong posterior consistency. Although we assume that f is defined over R^p, we show that this model has a convergence rate of log(n)^{-1} n^{-1/(d+2)} under the empirical L2 norm when f actually maps a d dimensional linear subspace to R. We design an efficient reversible jump MCMC algorithm for posterior computation and demonstrate the methods through application to value function approximation

Least Squares Fitting of Analytic Primitives on a GPU

Metrology systems take coordinate information directly from the surface of a manufactured part and generate millions of (X, Y, Z) data points. The inspection process often involves fitting analytic primitives such as sphere, cone, torus, cylinder and plane to these points which represent an object with the corresponding shape. Typically, a least squares fit of the parameters of the shape to the point set is performed. The least squares fit attempts to minimize the sum of the squares of the distances between the points and the primitive. The objective function however, cannot be solved in the closed form and numerical minimization techniques are required to obtain the solution. These techniques as applied to primitive fitting entail iteratively solving large systems of linear equations generally involving large floating point numbers until the solution has converged. The current problem in-process metrology faces is the large computational times for the analysis of these millions of streaming data points. This research addresses the bottleneck using the Graphical Processing Unit (GPU), primarily developed by the computer gaming industry, to optimize operations. The explosive growth in the programming capabilities and raw processing power of Graphical Processing Units has opened up new avenues for their use in non-graphic applications. The combination of large stream of data and the need for 3D vector operations make the primitive shape fit algorithms excellent candidates for processing via a GPU. The work presented in this research investigates the use of the parallel processing capabilities of the GPU in expediting specific computations involved in the fitting procedure. The least squares fit algorithms for the circle, sphere, cylinder, plane, cone and torus have been implemented on the GPU using NVIDIA\u27s Compute Unified Device Architecture (CUDA). The implementations are benchmarked against those on a CPU which are carried out using C++. The Gauss Newton minimization algorithm is used to obtain the best fit parameters for each of the aforementioned primitives. The computation times for the two implementations are compared. It is demonstrated that the GPU is about 3-4 times faster than the CPU for a relatively simple geometry such as the circle while the factor scales to about 14 for a torus which is more complex

Inside-out Infrared Marker Tracking via Head Mounted Displays for Smart Robot Programming

Intuitive robot programming through use of tracked smart input devices relies on fixed, external tracking systems, most often employing infra-red markers. Such an approach is frequently combined with projector-based augmented reality for better visualisation and interface. The combined system, although providing an intuitive programming platform with short cycle times even for inexperienced users, is immobile, expensive and requires extensive calibration. When faced with a changing environment and large number of robots it becomes sorely impractical. Here we present our work on infra-red marker tracking using the Microsoft HoloLens head-mounted display. The HoloLens can map the environment, register the robot on-line, and track smart devices equipped with infra-red markers in the robot coordinate system. We envision our work to provide the basis to transfer many of the paradigms developed over the years for systems requiring a projector and a tracked input device into a highly-portable system that does not require any calibration or special set-up. We test the quality of the marker-tracking in an industrial robot cell and compare our tracking with a ground truth obtained via an ART-3 tracking system.Comment: As accepted to the 5th International Workshop on Virtual, Augmented, and Mixed Reality for HR

A methodology for airplane parameter estimation and confidence interval determination in nonlinear estimation problems

An algorithm for maximum likelihood (ML) estimation is developed with an efficient method for approximating the sensitivities. The ML algorithm relies on a new optimization method referred to as a modified Newton-Raphson with estimated sensitivities (MNRES). MNRES determines sensitivities by using slope information from local surface approximations of each output variable in parameter space. With the fitted surface, sensitivity information can be updated at each iteration with less computational effort than that required by either a finite-difference method or integration of the analytically determined sensitivity equations. MNRES eliminates the need to derive sensitivity equations for each new model, and thus provides flexibility to use model equations in any convenient format. A random search technique for determining the confidence limits of ML parameter estimates is applied to nonlinear estimation problems for airplanes. The confidence intervals obtained by the search are compared with Cramer-Rao (CR) bounds at the same confidence level. The degree of nonlinearity in the estimation problem is an important factor in the relationship between CR bounds and the error bounds determined by the search technique. Beale's measure of nonlinearity is developed in this study for airplane identification problems; it is used to empirically correct confidence levels and to predict the degree of agreement between CR bounds and search estimates

Reconstructing triangulated surfaces from unorganized points through local skeletal stars

Surface reconstruction from unorganized points arises in a variety of practical situations such as range scanning an object from multiple view points, recovery of biological shapes from twodimensional slices, and interactive surface sketching. [...]Reconstrução da superfície de pontos desorganizados surge em uma variedade de situações práticas, tais como rastreamento de um objeto a partir de vários pontos de vista, a recuperação de formas biológicas de fatias bi-dimensionais, e esboçar superfícies interativas. [...

Decision support system for form verification of manufactured parts.

The form verification of manufactured parts is a process composed of a set of operations that are expensive and yet add no value to the product. Yet, the resources used to inspect the parts add a small but significant amount of noise that can affect the outcome of the process. For this reason, this research provides guidelines to effectively perform the inspection process by suggesting new mathematical models and approaches that can be used for the creation of a decision support system that can assist in the verification of the accuracy of machined parts.This research proposes two approaches to improve the robustness of the mathematical models from the noise induced by the inspection process. The Dynamic Angle Approach (DAA) and the Free Form Orientation approach (FFO) presented here focus on finding the parameters of the axes and origin of the form that counteract the inaccuracies of the inspection equipment.In summary, this research suggests formalized methods for feature extraction, sampling, path planning, and form fitting, although the last mentioned received the most attention. It is believed that this comprehensive, integrated analysis will lead to the development of a decision support system.The proposed approaches and mathematical models were verified using measurements from features that were perfectly aligned with the coordinate system of the inspection equipment and from features that were intentionally misaligned. The results showed that the models were accurate and robust enough to estimate the parameters and zone of error of the form features and they performed better than existing models.The main goal of this research is to develop procedures that are simple to implement but at the same time are robust enough to provide reliable information that help the metrologist to make accurate decisions about the inspected parts. Form features such as spheres, cylinders, cones, frustums, and torus forms are commonly used to design complex parts. However, the procedures to verify most of these form features have not been developed yet by the national standards. Therefore, this research proposes new mathematical models that combine the concepts of analytic geometry and optimization to provide optimal solutions

Vision-assisted robotic finishing of friction stir-welded corner joints

One required process in the fabrication of large components is welding, after which there may be a need for machining to achieve final dimensions and uniform surfaces. Friction stir-welding (FSW) is a typical example after which a series of deburring and grinding operations are carried out. Currently, the majority of these operations are carried out either manually, by human workers, or on machine tools which results in bottlenecks in the process flows. This paper presents a robotic finishing system to automate the finishing of friction stir-welded parts with minimum human involvement. In a sequence, the system can scan and reconstruct the 3D model of the part, localise it in the robot frame and generate a suitable machining path accordingly, to remove the excess material from FSW without violating process constraints. Results of the cutting trials carried out for demonstration have shown that the developed system can consistently machine the corner joints of an industrial scale part to desired surface quality which is around 1.25 μm in, Ra, the arithmetic average of the surface roughness