Algorithm for efficient 3D reconstruction of outdoor environments using mobile robots

In this paper, an algorithm for the reconstruction of an outdoor environment using a mobile robot is presented. The focus of this algorithm is making the mapping process efficient by capturing the greatest amount of information on every scan, ensuring at the same time that the overall quality of the resulting 3D model of the environment complies with the specified standards. With respect to existing approaches, the proposed approach is an innovation since there are very few information based methods for outdoor reconstruction that use resulting model quality and trajectory cost estimation as criteria for view planning

A SERVER-CLIENT SYSTEM FOR OPTIMIZED PLANNING OF OUTDOOR 3D LASER SCANNING

Effective and practical planning for laser scanning of outdoor construction is a challenging task. The selection of scanner positions according to on-site conditions within a limited time typically depends on ad-hoc procedures based on an operator's personal experiences. By using mathematical programming, the authors have been developing a planning technique to obtain minimum scan positions and their best configuration as an optimized solution. This technique takes into account the visibilities of the target object from every candidate scan position, based on precedent information such as 2D plans of the jobsite or primitive 3D models based on photogrammetry before starting the on-site scanning. Because existing applications cannot handle replanning and additional jobsite conditions, adhering to a prepared plan is often difficult. This paper proposes a mobile application to deal with the replanning functionality. Using a server-client system implementation, the proposed method transfers the high computational capability of the server to a mobile client at a jobsite

Prototype Robot for Computer Vision and Control Systems Applications

This paper describes a robot designed and developed by a student in the context of an Electronic Engineering degree course. This robot is composed by three wheels, two of them can be controlled inde- pendently and the third one is used for stability. The robot also includes a webcam provided with pan and tilt control. This work was focused on the implementation of a prototype useful for academic research in the areas of Computer Vision and Control Systems Dynamics. In this document, the main characteristics of this robot are described.Sociedad Argentina de Informática e Investigación Operativa (SADIO

Plataforma​ ​Robótica​ Para​ Tareas​ de​ Reconstrucción​ Tridimensional​ de​​ Entornos Exteriores

Este artículo presenta los resultados obtenidos en el diseño e implementación de una plataforma robótica todoterreno para la investigación y el desarrollo de aplicaciones de robótica de servicios en entornos exteriores, con especial énfasis en las tareas de reconstrucción tridimensional del entorno. En el documento se describe la estructura mecánica del robot, su arquitectura hardware-software y de comunicaciones y los sistemas perceptivos embarcados. Finalmente, como aportación adicional se presenta un algoritmo diseñado específicamente para llevar a cabo la reconstrucción tridimensional automática y eficiente del entorno, que opera sin necesidad de información previa sobre el mismo. Los resultados avalan la funcionalidad tanto de la plataforma robótica en sí, como de los algoritmos de adquisición y alineación de la información tridimensional, así como de selección automática de las mejores​ ​ posiciones​ ​ de​ ​ escaneo

Optimizing laser scanning positions in buildings exteriors: heritage building application

Digital documentation for heritage buildings is one of the methods of preserving them as it provides a current record for the buildings. Digital records of heritage buildings can be used for future building rehabilitation, or be presented to the public to raise the awareness, increase tourism and decrease vandalism. This paper focuses on scanning object geometry factor to increase the quality of heritage’s façade point cloud. It optimizes the scanner locations and the scanner field of view to increase the point cloud quality and shorten the scanning time while guaranteeing a set of quality constraints for the point cloud. The quality constraints are based on the incidence angle between the scanned surface and the laser beam, and the max spacing between points. Three different multi-objective optimization algorithms are utilized: 1) genetic algorithm, 2) Jaya algorithm, and 3) particle swarm optimization to increase the quality. Optimization performance measures are adopted to compare the outputs of the optimization algorithms. A multi-criteria decision-making technique (Weighed sum model) is used to choose the optimum solution between the Pareto frontier solutions. Optimization algorithms minimize point cloud density and scanning time while assuring a required point spacing and max incidence angle by changing distance between laser scanner and scanned Facade, horizontal and vertical scan repetitions, and scanner different resolutions. The Jaya algorithm generates the most diversifiable optimal solutions and it is the fastest of the three algorithms considered. This research focuses on vertical building façade and future research will include the all types of Heritage façade. Omar Tosson Palace in Egypt is considered as a case study to demonstrate the use of the developed methodology and to illustrate its essential features. First published online 25 February 202

A new method for efficient three-dimensional reconstruction of outdoor environments using mobile robots

In this paper, a method for robotic exploration oriented to the automatic three-dimensional (3D) reconstruction of outdoor scenes is presented. The proposed algorithm focuses on optimizing the exploration process by maximizing map quality, while reducing the number of scans required to create a good-quality 3D model of the environment. This is done by using expected information gain, expected model quality, and trajectory cost estimation as criteria for view planning. The method has been tested with an all-terrain mobile robot, which is also described in the paper. This robot is equipped with a SICK LMS 111 laser scanner attached to a spinning turret, which performs quick and complete all-around scans. Different experiments of autonomous 3D exploration show the suitable performance of the proposed exploration algorithm

An Approach Of Automatic Reconstruction Of Building Models For Virtual Cities From Open Resources

Along with the ever-increasing popularity of virtual reality technology in recent years, 3D city models have been used in different applications, such as urban planning, disaster management, tourism, entertainment, and video games. Currently, those models are mainly reconstructed from access-restricted data sources such as LiDAR point clouds, airborne images, satellite images, and UAV (uncrewed air vehicle) images with a focus on structural illustration of buildings’ contours and layouts. To help make 3D models closer to their real-life counterparts, this thesis research proposes a new approach for the automatic reconstruction of building models from open resources. In this approach, first, building shapes are reconstructed by using the structural and geographic information retrievable from the open repository of OpenStreetMap (OSM). Later, images available from the street view of Google maps are used to extract information of the exterior appearance of buildings for texture mapping onto their boundaries. The constructed 3D environment is used as prior knowledge for the navigation purposes in a self-driving car. The static objects from the 3D model are compared with the real-time images of static objects to reduce the computation time by eliminating them from the detection proces

Active Object Classification from 3D Range Data with Mobile Robots

This thesis addresses the problem of how to improve the acquisition of 3D range data with a mobile robot for the task of object classification. Establishing the identities of objects in unknown environments is fundamental for robotic systems and helps enable many abilities such as grasping, manipulation, or semantic mapping. Objects are recognised by data obtained from sensor observations, however, data is highly dependent on viewpoint; the variation in position and orientation of the sensor relative to an object can result in large variation in the perception quality. Additionally, cluttered environments present a further challenge because key data may be missing. These issues are not always solved by traditional passive systems where data are collected from a fixed navigation process then fed into a perception pipeline. This thesis considers an active approach to data collection by deciding where is most appropriate to make observations for the perception task. The core contributions of this thesis are a non-myopic planning strategy to collect data efficiently under resource constraints, and supporting viewpoint prediction and evaluation methods for object classification. Our approach to planning uses Monte Carlo methods coupled with a classifier based on non-parametric Bayesian regression. We present a novel anytime and non-myopic planning algorithm, Monte Carlo active perception, that extends Monte Carlo tree search to partially observable environments and the active perception problem. This is combined with a particle-based estimation process and a learned observation likelihood model that uses Gaussian process regression. To support planning, we present 3D point cloud prediction algorithms and utility functions that measure the quality of viewpoints by their discriminatory ability and effectiveness under occlusion. The utility of viewpoints is quantified by information-theoretic metrics, such as mutual information, and an alternative utility function that exploits learned data is developed for special cases. The algorithms in this thesis are demonstrated in a variety of scenarios. We extensively test our online planning and classification methods in simulation as well as with indoor and outdoor datasets. Furthermore, we perform hardware experiments with different mobile platforms equipped with different types of sensors. Most significantly, our hardware experiments with an outdoor robot are to our knowledge the first demonstrations of online active perception in a real outdoor environment. Active perception has broad significance in many applications. This thesis emphasises the advantages of an active approach to object classification and presents its assimilation with a wide range of robotic systems, sensors, and perception algorithms. By demonstration of performance enhancements and diversity, our hope is that the concept of considering perception and planning in an integrated manner will be of benefit in improving current systems that rely on passive data collection