Roberto Marani

A Modified Iterative Closest Point Algorithm for 3D Point Cloud Registration

In this article, an accurate method for the registration of point clouds returned by a 3D rangefinder is presented. The method modifies the well-known iterative closest point (ICP) algorithm by introducing the concept of deletion mask. This term is defined starting from virtual scans of the reconstructed surfaces and using inconsistencies between measurements. In this way, spatial regions of implicit ambiguities, due to edge effects or systematical errors of the rangefinder, are automatically found. Several experiments are performed to compare the proposed method with three ICP variants. Results prove the capability of deletion masks to aid the point cloud registration, lowering the errors of the other ICP variants, regardless the presence of artifacts caused by small changes of the sensor view-point and changes of the environment.

Recent trends in gesture recognition

This paper analyzes with a new perspective the recent state of-the-art on gesture recognition approaches that exploit both RGB and depth data (RGB-D images). The most relevant papers have been analyzed to point out which features and classifiers best work with depth data, if these fundamentals are specifically designed to process RGB-D images and, above all, how depth information can improve gesture recognition beyond the limit of standard approaches based on solely color images. Papers have been deeply reviewed finding the relation between gesture complexity and features/methodologies suitability. Different types of gestures are discussed, focusing attention on the kind of datasets (public or private) used to compare results, in order to understand weather they provide a good representation of actual challenging problems, such as: gesture segmentation, idle gesture recognition, and length gesture invariance. Finally the paper discusses on the current open problems and highlights the future directions of research in the field of processing of RGB-D data for gesture recognition. State of-the-art on gesture recognition approaches that exploit both RGB and depth data (RGB-D images)Analysis of different featuresAnalysis of classification methodsRelation between gesture complexity and features/methodologies suitabilityComprehensive discussion and future trends of research

An Embedded Vision System for Real-Time Autonomous Localization Using Laser Profilometry

In this paper, we propose an embedded vision system based on laser profilometry able to get the pose of a vehicle and its relative displacements with reference to the constitutive media of a structured environment. Fundamental equations for laser triangulation are developed and encoded for their actual implementation on an embedded system. It is made of a laser source that projects a line-shaped beam onto the environment and an on-chip camera able to frame the laser light. Images are then sent to the inexpensive Raspberry Pi onboard computer, which is responsible for processing tasks. For the first time, laser profilometry is coupled with the correlation of laser signatures on a low-cost and low-resource processing board for vehicle localization purposes. Several validation tests of the proposed sensor have proven the effectiveness of the system with respect to commercially available sensors such as inductive sensors and standard odometers, which fail when the vehicle crosses path interceptions or its wheels undergo unavoidable slippages. Moreover, further comparisons with other vision-based techniques have also proven the good performances of this embedded system for real-time localization of vehicles.

High-Resolution Laser Scanning for Three-Dimensional Inspection of Drilling Tools

A high-resolution vision system for the inspection of drilling tools is presented. A triangulation-based laser scanner is used to extract a three-dimensional model of the target aimed to the fast detection and characterization of surface defects. The use of two orthogonal calibrated handlings allows the achievement of precisions of the order of few microns in the whole testing volume and the prevention of self-occlusions induced on the undercut surfaces of the tool. Point cloud registration is also derived analytically to increase to strength of the measurement scheme, whereas proper filters are used to delete samples whose quality is below a reference threshold. Experimental tests are performed on calibrated spheres and different-sized tools, proving the capability of the presented setup to entirely reconstruct complex targets with maximum absolute errors between the estimated distances and the corresponding nominal values below 12 μm.

A Compact 3D Omnidirectional Range Sensor of High Resolution for Robust Reconstruction of Environments

In this paper, an accurate range sensor for the three-dimensional reconstruction of environments is designed and developed. Following the principles of laser profilometry, the device exploits a set of optical transmitters able to project a laser line on the environment. A high-resolution and high-frame-rate camera assisted by a telecentric lens collects the laser light reflected by a parabolic mirror, whose shape is designed ad hoc to achieve a maximum measurement error of 10 mm when the target is placed 3 m away from the laser source. Measurements are derived by means of an analytical model, whose parameters are estimated during a preliminary calibration phase. Geometrical parameters, analytical modeling and image processing steps are validated through several experiments, which indicate the capability of the proposed device to recover the shape of a target with high accuracy. Experimental measurements show Gaussian statistics, having standard deviation of 1.74 mm within the measurable range. Results prove that the presented range sensor is a good candidate for environmental inspections and measurements.

An adaptive parallel background model for high-throughput video applications and smart cameras embedding

Background (BG) modelling is a key task in every computer vision system (CVS) independently of the final purpose for which it is designed. Even if many BG approaches exist (for example Mixture of Gaussians or Eigenbackground), they can not efficiently process real time videos due to the model complexity and to the high throughput of the video flux. One of the most challenging real time applications is the athletic scene processing, because, in this context, there are many critical aspects for defining a BG model: no a-priori knowledge of the static scene, sudden illumination changes and many moving objects that slow down the upgrade phase. The aim of this work is to provide an adaptive BG model able to deal with high frame rate videos (≥ 100 fps) in real time processing, and suitable for smart cameras embedding, finding a good compromise between the model complexity and its responsiveness. Real experiments demonstrate that this BG model approach shows great performances and robustness during the real time processing of athletic video frames, up to 100 fps.

Automatic detection of subsurface defects in composite materials using thermography and unsupervised machine learning

This paper presents a complete framework aimed to nondestructive inspection of composite materials. Starting from the acquisition, performed with lock-in thermography, the method flows through a set of consecutive blocks of data processing: input enhancement, feature extraction, classification and defect detection. Experimental results prove the capability of the presented methodology to detect the presence of defects underneath the surface of a calibrated specimen made of Glass Fiber Reinforced Polymer (GFRP). Results are also compared with those obtained by other techniques, based on different features and unsupervised learning methods. The comparison further proves that the proposed methodology is able to reduce the number of false positives, while ensuring the exact detection of subsurface defects.

Automated Extraction of Archaeological Traces by a Modified Variance Analysis

This paper considers the problem of detecting archaeological traces in digital aerial images by analyzing the pixel variance over regions around selected points. In order to decide if a point belongs to an archaeological trace or not, its surrounding regions are considered. The one-way ANalysis Of VAriance (ANOVA) is applied several times to detect the differences among these regions; in particular the expected shape of the mark to be detected is used in each region. Furthermore, an effect size parameter is defined by comparing the statistics of these regions with the statistics of the entire population in order to measure how strongly the trace is appreciable. Experiments on synthetic and real images demonstrate the effectiveness of the proposed approach with respect to some state-of-the-art methodologies.

Monitoring of indoor environments by change detection in point clouds

This paper describes a complete method for monitoring indoor environments. Three-dimensional (3D) point clouds are first acquired from the environment under investigation by means of a laser range scanner in order to obtain several 3D models to be compared. Input datasets are thus registered each other exploiting a reliable variant of the iterative closest point algorithm (ICP) assisted by the use of deletion masks. These terms work in cooperation with the resampling of the model surfaces to reduce significantly the errors in the estimation of the registration parameters. Once datasets are registered, deformation maps are displayed to help the user to detect changes within the environment. Deletion masks are again used to filter measurement artifacts from the comparison, thus highlighting only the actual alterations of the environment. Several experiments are performed for the analysis of an indoor environment, proving the capability of the proposed method to reliably estimate the presence of alterations.

Design of a Low-Cost Vision System for Laser Profilometry Aiding Smart Vehicles Movement

We present a fast and accurate method to derive the pose of a mobile vehicle moving within bounded paths. A triangulation-based vision system made of a laser source, able to generate a line pattern, and a high speed camera is applied on the front side of an autonomous vehicle, namely the Smoov ASRV platform, which is able to store and retrieve pallets in smart warehouses. The presented system extracts the properties of the emitted laser line on the camera plane and transfers these information to the vehicle reference system. Then, the presence of constitutive landmarks along the path, i.e., holes and bends, permit the estimation of other parameters, such as vehicle speed, enabling the exact control of the vehicle. Further validations have returned accuracies lower than 2 and 3.2 % in distance and tilt measurements with respect to the rail border, respectively.