Performance Investigation and Repeatability Assessment of a Mobile Robotic System for 3D Mapping

In this paper, we present a quantitative performance investigation and repeatability assessment of a mobile robotic system for 3D mapping. With the aim of a more efficient and automatic data acquisition process with respect to well-established manual topographic operations, a 3D laser scanner coupled with an inertial measurement unit is installed on a mobile platform and used to perform a high-resolution mapping of the surrounding environment. Point clouds obtained with the use of a mobile robot are compared with those acquired with the device carried manually as well as with a terrestrial laser scanner survey that serves as a ground truth. Experimental results show that both mapping modes provide similar accuracy and repeatability, whereas the robotic system compares favorably with respect to the handheld modality in terms of noise level and point distribution. The outcomes demonstrate the feasibility of the mobile robotic platform as a promising technology for automatic and accurate 3D mapping

Smartphone-based food diagnostic technologies: A review

A new generation of mobile sensing approaches offers significant advantages over traditional platforms in terms of test speed, control, low cost, ease-of-operation, and data management, and requires minimal equipment and user involvement. The marriage of novel sensing technologies with cellphones enables the development of powerful lab-on-smartphone platforms for many important applications including medical diagnosis, environmental monitoring, and food safety analysis. This paper reviews the recent advancements and developments in the field of smartphone-based food diagnostic technologies, with an emphasis on custom modules to enhance smartphone sensing capabilities. These devices typically comprise multiple components such as detectors, sample processors, disposable chips, batteries and software, which are integrated with a commercial smartphone. One of the most important aspects of developing these systems is the integration of these components onto a compact and lightweight platform that requires minimal power. To date, researchers have demonstrated several promising approaches employing various sensing techniques and device configurations. We aim to provide a systematic classification according to the detection strategy, providing a critical discussion of strengths and weaknesses. We have also extended the analysis to the food scanning devices that are increasingly populating the Internet of Things (IoT) market, demonstrating how this field is indeed promising, as the research outputs are quickly capitalized on new start-up companies

INTEGRATION OF PHOTOGRAMMETRY AND PORTABLE MOBILE MAPPING TECHNOLOGY FOR 3D MODELING OF CULTURAL HERITAGE SITES: THE CASE STUDY OF THE BZIZA TEMPLE

Abstract. In this paper, we present a multi-sensor approach employed to obtain the 3D model of the Roman temple of Bziza (Lebanon) and its surroundings, a work carried out as part of the archaeological Northern Lebanon Project (NoLeP). The integration of photogrammetry and portable mobile mapping technology was tested to overcome the weaknesses of each individual surveying method, with the aim of producing a complete and realistic 3D reconstruction of the whole site, as well as capturing at high-resolution the architectural features of the main structure. Moreover, this case study serves to further investigate the accuracy that can be reached with mobile laser scanners, highlighting benefits and limitations of this rapid and efficient mapping technique also in the field of Cultural Heritage documentation

MAPPING BUILDING INTERIORS WITH LIDAR: CLASSIFYING THE POINT CLOUD WITH ARCGIS

Accurate maps of building interiors are needed to support location-based services, plan for emergencies, and manage facilities. However, suitable maps to meet these needs are not available for many buildings. Handheld LiDAR scanners provide an effective tool to collect data for indoor mapping but there are no well-established methods for classifying features in indoor point clouds. The goal of this research was to develop an efficient manual procedure for classifying indoor point clouds to represent features-of-interest. We used Paracosm’s PX-80 handheld LiDAR scanner to collect point cloud and image data for 11 buildings, which encompassed a variety of architectures. ESRI’s ArcGIS Desktop was used to digitize features that were easily identified in the point cloud and Paracosm’s Retrace was used to digitize features for which imagery was needed for efficient identification. We developed several tools in Python to facilitate the process. We focused on classifying 29 features-of-interest to public safety personnel including walls, doors, windows, fire alarms, smoke detectors, and sprinklers. The method we developed was efficient, accurate, and allowed successful mapping of features as small as a sprinkler head. Point cloud classification for a 14,000 m2 building took 20–40 hours, depending on building characteristics. Although the method is based on manual digitization, it provides a practical solution for indoor mapping using LiDAR. The methods can be applied in mapping a wide variety of features in indoor or outdoor environments

3D MODELS FOR ALL: LOW-COST ACQUISITION THROUGH MOBILE DEVICES IN COMPARISON WITH IMAGE BASED TECHNIQUES. POTENTIALITIES AND WEAKNESSES IN CULTURAL HERITAGE DOMAIN

Nowadays, 3D digital imaging proposes effective solutions for preserving the expression of human creativity across the centuries, as well as is a great tool to guarantee global dissemination of knowledge and wide access to these invaluable resources of the past. Nevertheless, in several cases, a massive digitalisation of cultural heritage items (from the archaeological site up to the monument and museum collections) could be unworkable due to the still high costs in terms of equipment and human resources: 3D acquisition technologies and the need of skilled team within cultural institutions. Therefore, it is necessary to explore new possibilities offered by growing technologies: the lower costs of these technologies as well as their attractive visual quality constitute a challenge for researchers. Besides these possibilities, it is also important to consider how information is spread through graphic representation of knowledge. The focus of this study is to explore the potentialities and weaknesses of a newly released low cost device in the cultural heritage domain, trying to understand its effective usability in museum collections. The aim of the research is to test their usability, critically analysing the final outcomes of this entry level technology in relation to the other better assessed low cost technologies for 3D scanning, such as Structure from Motion (SfM) techniques (also produced by the same device) combined with dataset generated by a professional digital camera. The final outcomes were compared in terms of quality definition, time processing and file size. The specimens of the collections of the Civic Museum Castello Ursino in Catania have been chosen as the site of experimentation

Point clouds by SLAM-based mobile mapping systems: accuracy and geometric content validation in multisensor survey and stand-alone acquisition

The paper provides some operative replies to evaluate the effectiveness and the critical issues of the simultaneous localisation and mapping (SLAM)-based mobile mapping system (MMS) called ZEB by GeoSLAM™ https://geoslam.com/technology/. In these last years, this type of handheld 3D mapping technology has increasingly developed the framework of portable solutions for close-range mapping systems that have mainly been devoted to mapping the indoor building spaces of enclosed or underground environments, such as forestry applications and tunnels or mines. The research introduces a set of test datasets related to the documentation of landscape contexts or the 3D modelling of architectural complexes. These datasets are used to validate the accuracy and informative content richness about ZEB point clouds in stand-alone solutions and in cases of combined applications of this technology with multisensor survey approaches. In detail, the proposed validation method follows the fulfilment of the endorsed approach by use of root mean square error (RMSE) evaluation and deviation analysis assessment of point clouds between SLAM-based data and 3D point cloud surfaces computed by more precise measurement methods to evaluate the accuracy of the proposed approach. Furthermore, this study specifies the suitable scale for possible handlings about these peculiar point clouds and uses the profile extraction method in addition to feature analyses such as corner and plane deviation analysis of architectural elements. Finally, because of the experiences reported in the literature and performed in this work, a possible reversal is suggested. If in the 2000s, most studies focused on intelligently reducing the light detection and ranging (LiDAR) point clouds where they presented redundant and not useful information, contrariwise, in this sense, the use of MMS methods is proposed to be firstly considered and then to increase the information only wherever needed with more accurate high-scale methods

Face recognition with the RGB-D sensor

Face recognition in unconstrained environments is still a challenge, because of the many variations of the facial appearance due to changes in head pose, lighting conditions, facial expression, age, etc. This work addresses the problem of face recognition in the presence of 2D facial appearance variations caused by 3D head rotations. It explores the advantages of the recently developed consumer-level RGB-D cameras (e.g. Kinect). These cameras provide color and depth images at the same rate. They are affordable and easy to use, but the depth images are noisy and in low resolution, unlike laser scanned depth images. The proposed approach to face recognition is able to deal with large head pose variations using RGB-D face images. The method uses the depth information to correct the pose of the face. It does not need to learn a generic face model or make complex 3D-2D registrations. It is simple and fast, yet able to deal with large pose variations and perform pose-invariant face recognition. Experiments on a public database show that the presented approach is effective and efficient under significant pose changes. Also, the idea is used to develop a face recognition software that is able to achieve real-time face recognition in the presence of large yaw rotations using the Kinect sensor. It is shown in real-time how this method improves recognition accuracy and confidence level. This study demonstrates that RGB-D sensors are a promising tool that can lead to the development of robust pose-invariant face recognition systems under large pose variations