Marcelo Petry

Abstract
The transport of patients from the inpatient service to the operating room is a recurrent task in the hospital routine. This task is repetitive, non-ergonomic, time consuming, and requires the labor of patient transporters. In this paper is presented the design of a driverless wheelchair under development capable of providing an on-demand mobility service to hospitals. The proposed wheelchair can receive transportation requests directly from the hospital information management system, pick-up patients at their beds, navigate autonomously through different floors, avoid obstacles, communicate with elevators, and drop patients off at the designated destination. © 2020 IEEE.

Abstract
Localisation is a critical problem in ground mobile robots. For dead reckoning, odometry is usually used. A disadvantage of using it alone is unbounded error accumulation. So, odometry calibration is critical in reducing error propagation. This paper presents an analysis of the developments and advances of systematic methods for odometry calibration. Four steering geometries were analysed, namely differential drive, Ackerman, tricycle and omnidirectional. It highlights the advances made on this field and covers the methods since UMBmark was proposed. The points of analysis are the techniques and test paths used, errors considered in calibration, and experiments made to validate each method. It was obtained fifteen methods for differential drive, three for Ackerman, two for tricycle, and three for the omnidirectional steering geometry. A disparity was noted, compared with the real utilisation, between the number of published works addressing differential drive and tricycle/Ackerman. Still, odometry continues evolving since UMBmark was proposed. © 2020 IEEE.

Abstract
Optical character recognition (OCR) is the process by which the textual content of an image is converted into strings. Localization is the problem of figuring out where one is in a given environment. In this work we approach the application of OCR in robot localization. We develop and test a vision based localization system that is capable of detecting room identification signs present in the environment, recognizing their textual contents and apply them to determine its location referent to a topological map of the environment. A sign detection method based on image segmentation by color and corner detection by contour analysis is developed. The recognition of characters is performed with the application of an open-source OCR engine. Localization is performed through the comparison of sign readings with the textual information embedded in the topological representation of the environment. The algorithm was tested in a dataset of images acquired in a corridor. Experimental results show that the system successfully determines its localization in 83.33% of tested cases. © 2019 IEEE.

Abstract
The photogrammetry, 3D reconstruction from images, is an old technique but it's potentials could only be seen after the development of computers and digital photographs. Nowadays it has many applications, as creating scenarios for games, acquiring human expressions, roof inspection, stockpile measurement, high voltage transformer inspection, etc. As new technologies appear, new applications to photogrammetry are created. In this paper the use of available open and closed-source algorithms for 3D reconstruction and texturization is investigated. To achieve this goal, images of a fountain from several points-of-view were used. Next a comparison between several open and closed-source algorithms was performed, evaluating the number of faces, time consumption, RAM memory, GPU memory and the generated textured 3D models. The results obtained demonstrate that with the right setup, current open-source algorithms can achieve results near or better than proprietary software. Regarding the comparison, 3Dflow and MeshRecon presented the most accurate textured 3D models. When comparing quantitative measures, though, MeshRecon presented a slightly better performance in time consumption, but 3Dflow had a better RAM memory usage and a lower quantity of faces with a similar level of details. © 2019 IEEE.

Abstract
Inspection of railroad tracks is still predominantly performed visually by human inspectors. Due to the extension of the tracks this is a slow and tedious operation, significantly subjected to human errors and inconsistency. In this context, computer vision systems, composed of field-acquired images and processing algorithms, have a great potential to improve efficiency and to offer systematic inspection methodologies. In this paper the use of available point cloud and mesh generation algorithms to construct 3D surface of railroad tracks is investigated. To achieve this goal, images of a small track were acquired from several points-of-view. Next a comparison between several open and closed-source algorithms was performed, evaluating the number of 3D points, time consumption, RAM memory, GPU memory, number of faces, and the generated mesh. The results obtained demonstrate that with the right setup, current image processing methodologies can be used to construct 3D surfaces of uncontrolled scenarios, such as those of a real railroad environment. Regarding the comparison, SURE and Poisson presented the most accurate meshes. When comparing quantitative measures, though, Poisson presented a slightly better performance in time consumption, but SURE had a better RAM memory usage and a greater level of details. © 2017 IEEE.