A Graph-based Optimization Framework for Hand-Eye Calibration for Multi-Camera Setups

Hand-eye calibration is the problem of estimating the spatial transformation between a reference frame, usually the base of a robot arm or its gripper, and the reference frame of one or multiple cameras. Generally, this calibration is solved as a non-linear optimization problem, what instead is rarely done is to exploit the underlying graph structure of the problem itself. Actually, the problem of hand-eye calibration can be seen as an instance of the Simultaneous Localization and Mapping (SLAM) problem. Inspired by this fact, in this work we present a pose-graph approach to the hand-eye calibration problem that extends a recent state-of-the-art solution in two different ways: i) by formulating the solution to eye-on-base setups with one camera; ii) by covering multi-camera robotic setups. The proposed approach has been validated in simulation against standard hand-eye calibration methods. Moreover, a real application is shown. In both scenarios, the proposed approach overcomes all alternative methods. We release with this paper an open-source implementation of our graph-based optimization framework for multi-camera setups.Comment: This paper has been accepted for publication at the 2023 IEEE International Conference on Robotics and Automation (ICRA

Indagine statistica su acquisti e vendite dei principali prodotti alimentari locali nei Siti di Interesse Nazionale (SIN)

L’indagine nasce dalla collaborazione tra l’Istituto Zooprofilattico Sperimentale della Sicilia (IZS Sicilia) e l’Istituto per lo Studio degli Impatti Antropici e Sostenibilità in Ambiente Marino del Consiglio Nazionale delle Ricerche (IAS-CNR) nell’ambito del progetto CISAS (Centro Internazionale di Studi Avanzati su Ambiente, Ecosistema e Salute Umana). In CISAS sono state svolte attività indirizzate alla valutazione del rischio sanitario per le popolazioni residenti nelle aree dei Siti di Interesse Nazionale (SIN) di Augusta-Melilli-Priolo (SIN 1), di Milazzo (SIN 2) e di Crotone (SIN 3) caratterizzati da un forte degrado ambientale e da fenomeni di impatto umano di varia origine, sostanzialmente causati dallo sviluppo di importanti attività industriali (Giosuè et al., 2020). In questo scenario, considerati anche i risultati emersi da diversi rapporti sia italiani che Europei (ISMEA, 2019), i quali evidenziano una crescita di vendite dei prodotti di origine locale (certificati con marchi quali ad es. DOP, IGP, ecc..), spesso a km zero, si è ritenuto di fondamentale importanza indagare sul mercato dei prodotti di origine vegetale e animale provenienti dai tre SIN e destinati al mercato locale. Inoltre, tenendo in considerazione che negli ultimi anni la Grande Distribuzione Organizzata (GDO) ha avviato processi di acquisto e vendita di prodotti locali, prevedendo dei disciplinari di produzione, spesso particolarmente rigidi nel garantire la sicurezza alimentare, l’IZS Sicilia ha ritenuto di notevole importanza realizzare un’indagine esplorativa sui flussi di vendita e acquisti di prodotti freschi locali proprio nell’ambito della GDO, al fine di reperire informazioni più puntuali per implementare le valutazioni sul rischio delle popolazioni residenti nel SIN o nelle aree limitrofe. L’indagine è stata condotta attraverso interviste ai responsabili commerciali delle principali insegne della GDO, identificati su base campionaria nei SIN di Augusta Melilli Priolo, di Milazzo e di Crotone

METRIC—Multi-Eye to Robot Indoor Calibration Dataset

Multi-camera systems are an effective solution for perceiving large areas or complex scenarios with many occlusions. In such a setup, an accurate camera network calibration is crucial in order to localize scene elements with respect to a single reference frame shared by all the viewpoints of the network. This is particularly important in applications such as object detection and people tracking. Multi-camera calibration is a critical requirement also in several robotics scenarios, particularly those involving a robotic workcell equipped with a manipulator surrounded by multiple sensors. Within this scenario, the robot-world hand-eye calibration is an additional crucial element for determining the exact position of each camera with respect to the robot, in order to provide information about the surrounding workspace directly to the manipulator. Despite the importance of the calibration process in the two scenarios outlined above, namely (i) a camera network, and (ii) a camera network with a robot, there is a lack of standard datasets available in the literature to evaluate and compare calibration methods. Moreover they are usually treated separately and tested on dedicated setups. In this paper, we propose a general standard dataset acquired in a robotic workcell where calibration methods can be evaluated in two use cases: camera network calibration and robot-world hand-eye calibration. The Multi-Eye To Robot Indoor Calibration (METRIC) dataset consists of over 10,000 synthetic and real images of ChAruCo and checkerboard patterns, each one rigidly attached to the robot end-effector, which was moved in front of four cameras surrounding the manipulator from different viewpoints during the image acquisition. The real images in the dataset includes several multi-view image sets captured by three different types of sensor networks: Microsoft Kinect V2, Intel RealSense Depth D455 and Intel RealSense Lidar L515, to evaluate their advantages and disadvantages for calibration. Furthermore, in order to accurately analyze the effect of camera-robot distance on calibration, we acquired a comprehensive synthetic dataset, with related ground truth, with three different camera network setups corresponding to three levels of calibration difficulty depending on the cell size. An additional contribution of this work is to provide a comprehensive evaluation of state-of-the-art calibration methods using our dataset, highlighting their strengths and weaknesses, in order to outline two benchmarks for the two aforementioned use cases

Validation of a Commercial Loop-Mediated Isothermal Amplification (LAMP)-Based Kit for the Detection of Salmonella spp. According to ISO 16140:2016

The traditional cultural method (PCR and Real-Time PCR) for Salmonella spp. detection and identification is laborious and time-consuming. A qualitative LAMP method detecting Salmonella spp. was validated in compliance with ISO 16140:2016. The results show a relative accuracy, sensitivity, and specificity of 100% in comparison with the reference method ISO 6579-1:2017; the LOD50 was set as 0.4 CFU/g. Additionally, a field study was carried out comparing the LAMP kit, a commercially available Real-Time PCR kit (FoodProof Salmonella, Biotecon Diagnostics), and the reference cultural method. The Salmonella spp. LAMP kit was suitable for reliable detection of Salmonella spp., simplifying and reducing the extent and the steps of the analytical process. A total of 105 samples of raw poultry meat were screened for the presence of Salmonella spp. according to three methods: the LAMP kit Salmonella spp. (Enbiotech), the Real-Time PCR kit FoodProof Salmonella (Biotecon), and the reference cultural method. Using these three methods, only one sample out of the 105 (0.95%) tested was positive for Salmonella spp. This sample was further investigated using the reference method described in ISO 6579-3:2014, in order to characterise the Salmonella strain. Following this further biochemical identification and serological typing, the isolate was characterised as Salmonella Infantis

Drinking water source and cigarette smoking in transitional cell carcinoma of the bladder.

OBJECTIVE: Water source and cigarette smoking are related to clinical characteristics and pathology of superficial transitional cell carcinoma of the bladder. METHODS: Tumor number, dimension, G-grade, T-stage, recurrences, cigarette smoking and water supply were recorded in patients harboring Ta-T1 G1-3 transitional cell carcinoma of the bladder. RESULTS: Of 577 patients, 61% had multiple and 36% recurrent tumors. Two hundred and forty-one patients (42%) were current smokers and 188 (33%) were former smokers. Bottled water was the only drinkable source for 249 (45%) patients, municipal water supply for 177 (32%), artesian wells for 38 (7%), spring water for 7 (1%) and mixed source for 89 (16%). By adopting a cut-off of 30 years of smoking, patients affected by recurrent tumors varied from 22 to 43% (p = 0.0001). T1 tumors were more frequent in patients drinking nonbottled water (p = 0.03). Nonbottled supply was more frequent in never smokers (p = 0.015) and could represent a weak risk factor not detectable in smokers. CONCLUSIONS: Cigarette smoking correlates with the number of recurrences. T1 tumors were statistically more frequent in patients taking nonbottled drinking water. Chlorinated water supply was more frequent among patients who did not present cigarette smoking as a risk factor