Miguel Oliveira, Vítor Santos, Angel D. Sappa, Paulo Dias, & A. Paulo Moreira. (2016). Incremental Scenario Representations for Autonomous Driving using Geometric Polygonal Primitives. Robotics and Autonomous Systems Journal, Vol. 83, pp. 312–325.
Abstract: When an autonomous vehicle is traveling through some scenario it receives a continuous stream of sensor data. This sensor data arrives in an asynchronous fashion and often contains overlapping or redundant information. Thus, it is not trivial how a representation of the environment observed by the vehicle can be created and updated over time. This paper presents a novel methodology to compute an incremental 3D representation of a scenario from 3D range measurements. We propose to use macro scale polygonal primitives to model the scenario. This means that the representation of the scene is given as a list of large scale polygons that describe the geometric structure of the environment. Furthermore, we propose mechanisms designed to update the geometric polygonal primitives over time whenever fresh sensor data is collected. Results show that the approach is capable of producing accurate descriptions of the scene, and that it is computationally very efficient when compared to other reconstruction techniques.
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Monica Villavicencio, & Alain Abran. (2011). Educational Issues in the Teaching of Software Measurement in Software Engineering Undergraduate Programs. In Joint Conference of the International Workshop on Software Measurement and the International Conference on Software Process and Product Measurement (pp. 239–244). IEEE.
Abstract: In mature engineering disciplines and science, mathematics and measurement are considered as important subjects to be taught in university programs. This paper discusses about these subjects in terms of their respective meanings and complementarities. It also presents a discussion regarding their maturity, relevance and innovations in their teaching in engineering programs. This paper pays special attention to the teaching of software measurement in higher education, in particular with respect to mathematics and measurement in engineering in general. The findings from this analysis will be useful for researchers and educators interested in the enhancement of educational issues related to software measurement.
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Miguel Oliveira, Vítor Santos, Angel D. Sappa, Paulo Dias, & A. Paulo Moreira. (2016). Incremental Texture Mapping for Autonomous Driving. Robotics and Autonomous Systems Journal, Vol. 84, pp. 113–128.
Abstract: Autonomous vehicles have a large number of on-board sensors, not only for providing coverage all around the vehicle, but also to ensure multi-modality in the observation of the scene. Because of this, it is not trivial to come up with a single, unique representation that feeds from the data given by all these sensors. We propose an algorithm which is capable of mapping texture collected from vision based sensors onto a geometric description of the scenario constructed from data provided by 3D sensors. The algorithm uses a constrained Delaunay triangulation to produce a mesh which is updated using a specially devised sequence of operations. These enforce a partial configuration of the mesh that avoids bad quality textures and ensures that there are no gaps in the texture. Results show that this algorithm is capable of producing fine quality textures.
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Miguel Realpe, Boris X. Vintimilla, & Ljubo Vlacic. (2016). Multi-sensor Fusion Module in a Fault Tolerant Perception System for Autonomous Vehicles. Journal of Automation and Control Engineering (JOACE), Vol. 4, pp. 430–436.
Abstract: Driverless vehicles are currently being tested on public roads in order to examine their ability to perform in a safe and reliable way in real world situations. However, the long-term reliable operation of a vehicle’s diverse sensors and the effects of potential sensor faults in the vehicle system have not been tested yet. This paper is proposing a sensor fusion architecture that minimizes the influence of a sensor fault. Experimental results are presented simulating faults by introducing displacements in the sensor information from the KITTI dataset.
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Miguel Realpe, Boris X. Vintimilla, & Ljubo Vlacic. (2016). A Fault Tolerant Perception system for autonomous vehicles. In 35th Chinese Control Conference (CCC2016), International Conference on, Chengdu (pp. 1–6).
Abstract: Driverless vehicles are currently being tested on public roads in order to examine their ability to perform in a safe and reliable way in real world situations. However, the long-term reliable operation of a vehicle’s diverse sensors and the effects of potential sensor faults in the vehicle system have not been tested yet. This paper is proposing a sensor fusion architecture that minimizes the influence of a sensor fault. Experimental results are presented simulating faults by introducing displacements in the sensor information from the KITTI dataset.
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Juan A. Carvajal, Dennis G. Romero, & Angel D. Sappa. (2016). Fine-tuning based deep covolutional networks for lepidopterous genus recognition. In XXI IberoAmerican Congress on Pattern Recognition (pp. 1–9).
Abstract: This paper describes an image classication approach ori- ented to identify specimens of lepidopterous insects recognized at Ecuado- rian ecological reserves. This work seeks to contribute to studies in the area of biology about genus of butter ies and also to facilitate the reg- istration of unrecognized specimens. The proposed approach is based on the ne-tuning of three widely used pre-trained Convolutional Neural Networks (CNNs). This strategy is intended to overcome the reduced number of labeled images. Experimental results with a dataset labeled by expert biologists, is presented|a recognition accuracy above 92% is reached. 1 Introductio
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Angel D. Sappa, Cristhian A. Aguilera, Juan A. Carvajal Ayala, Miguel Oliveira, Dennis Romero, Boris X. Vintimilla, et al. (2016). Monocular visual odometry: a cross-spectral image fusion based approach. Robotics and Autonomous Systems Journal, Vol. 86, pp. 26–36.
Abstract: This manuscript evaluates the usage of fused cross-spectral images in a monocular visual odometry approach. Fused images are obtained through a Discrete Wavelet Transform (DWT) scheme, where the best setup is em- pirically obtained by means of a mutual information based evaluation met- ric. The objective is to have a exible scheme where fusion parameters are adapted according to the characteristics of the given images. Visual odom- etry is computed from the fused monocular images using an off the shelf approach. Experimental results using data sets obtained with two different platforms are presented. Additionally, comparison with a previous approach as well as with monocular-visible/infrared spectra are also provided showing the advantages of the proposed scheme.
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Angely Oyola, Dennis G. Romero, & Boris X. Vintimilla. (2017). A Dijkstra-based algorithm for selecting the Shortest-Safe Evacuation Routes in dynamic environments (SSER). In The 30th International Conference on Industrial, Engineering, Other Applications of Applied Intelligent Systems (IEA/AIE 2017) (pp. 131–135).
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Sianna Puente, Cindy Madrid, Miguel Realpe, & Boris X. Vintimilla. (2017). An Empirical Comparison of DCNN libraries to implement the Vision Module of a Danger Management System. In 2017 International Conference on Deep Learning Technologies (ICDLT 2017) (Vol. Part F128535, pp. 60–65).
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Patricia L. Suarez, Angel D. Sappa, & Boris X. Vintimilla. (2017). Cross-spectral Image Patch Similarity using Convolutional Neural Network. In 2017 IEEE International Workshop of Electronics, Control, Measurement, Signals and their application to Mechatronics (ECMSM) (pp. 1–5).
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