POINT CLOUD REGISTRATION AND ACCURACY FOR 3D MODELLING - A REVIEW

Geoinformation is a surveying and mapping field where topography and details on the ground are spatially mapped. The point cloud is one of the data types that is used for measurement and visualisation of Earth features mapping. Point cloud could come from a different source such as terrestrial laser scanned or photogrammetry. The concepts of terrestrial laser scanning and photogrammetry surveying are elaborated in this paper. This paper also presents the method used for point cloud registration; Iterative Closest Point (ICP) and Feature Extraction and Matching (FEM) and the accuracy of laser scanned, and photogrammetric point cloud based on the previous experiments. Experimental analysis conducted in the previous study shows an impressive result on laser scanned point cloud with very mínimum errors compared to photogrammetric point cloud.

A Novel Digital Technique for Measuring the Accuracy of an Indirect Bonding Technique Using Fixed Buccal Multibracket Appliances

The aim of this study was to analyze the accuracy and predictability of the indirect bonding technique of fixed buccal multibracket appliances using a customized iterative closest point algorithm. Materials and Methods: A total of 340 fixed buccal multibracket appliances were virtually planned and bonded on 34 experimental anatomically based acrylic resin models by using orthodontic templates designed and manufactured to indirectly bond the fixed buccal multibracket appliances. Afterwards, the models were submitted to a three-dimensional impression technique by an intraoral scanner, and the standard tessellation language digital files from the virtual planning and the digital impression were aligned, segmented, and realigned using morphometric software. Linear positioning deviations (mm) of the fixed buccal multibracket appliances were quantified at mesio-distal, bucco-lingual/palatal, and gingival/occlusal (vertical) planes, and angular deviations (°) were also recorded by analyzing the torque, tip, and rotation using a customized iterative closest point algorithm, the script for which allowed for an accuracy measurement procedure by comparing the tessellation network positioning of both standard tessellation language digital files. Results: The mean mesio-distal deviation was −0.065 ± 0.081 mm, the mean bucco-lingual/palatal deviation was 0.129 ± 0.06 m, the mean vertical deviation was −0.094 ± 0.147 mm, the mean torque deviation was −0.826 ± 1.721°, the mean tip deviation was −0.271 ± 0.920°, and the mean rotation deviation was −0.707 ± 0.648°. Conclusion: The indirect bonding technique provides accurate and predictable positioning of fixed buccal multibracket appliances.

Nut Unfastening by Robotic Surface Exploration

In this paper, we present a novel concept and primary investigations regarding automated unfastening of hexagonal nuts by means of surface exploration with a compliant robot. In contrast to the conventional industrial approaches that rely on custom-designed motorised tools and mechanical tool changers, we propose to use robot fingers to position, grasp and unfasten unknown random-sized hexagonal nuts, which are arbitrarily positioned in the robot’s task space. Inspired by how visually impaired people handle unknown objects, in this work, we use information observed from surface exploration to devise the unfastening strategy. It combines torque monitoring with active compliance for the robot fingers to smoothly explore the object’s surface. We implement a shape estimation technique combining scaled iterative closest point and hypotrochoid approximation to estimate the location as well as contour profile of the hexagonal nut so as to accurately position the gripper fingers. We demonstrate this work in the context of dismantling an electrically driven vehicle battery pack. The experiments are conducted using a seven degrees of freedom (DoF)–compliant robot fitted with a two-finger gripper to unfasten four different sized randomly positioned hexagonal nuts. The obtained results suggest an overall exploration and unfastening success rate of 95% over an average of ten trials for each nut.

Avaliação do Desempenho dos Métodos ICP, CPD e SVR para Registro Automático de Nuvens de Pontos Relativas a Telhados Extraídas de Dados LiDAR Aerotransportados

A partir dos anos 2000, houve um aumento na aquisição de dados LiDAR (Light Detection and Ranging) em áreas urbanas, o que possibilitou diversos estudos e aplicações nas mais variadas áreas, verificando-se um crescimento dos acervos históricos. Com isso, são necessários métodos de processamento robustos para manipulação desses dados. Os métodos de registro de dados laser inserem-se nesse contexto, essenciais para promover a utilização de dados oriundos de distintos equipamentos e datas. Este estudo consiste em avaliar o desempenho de três métodos de registro: Iterative Closest Point (ICP), Coherent Point Drift (CPD) e Support Vector Registration (SVR). A metodologia contempla o pré-processamento dos dados LiDAR para a extração de três telhados de edifícios com características distintas, localizados no campus da UFPR, em Curitiba – PR. Foram utilizados dados do sensor Optech ALTM Pegasus HD 500, com frequência de 300 kHz e altura de voo de 1.600 m, densidade média de 1,71 pontos por m² e IFOV de 25°. Os métodos foram implementados na linguagem Python. Como resultados, foram obtidos os registros, dos quais foram extraídas suas acurácias e tempos de processamento. Os resultados evidenciaram que os métodos CPD e SVR são ótimas alternativas para superar as limitações do ICP, ressaltando-se o desempenho do CPD e a eficiência computacional do SVR, sendo que este último é particularmente adequado para lidar com dados ruidosos.