2018 |
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Alex Ferrin, Julio Larrea, Miguel Realpe, & Daniel Ochoa. (2018). Detection of utility poles from noisy Point Cloud Data in Urban environments. In Artificial Intelligence and Cloud Computing Conference (AICCC 2018) (pp. 53–57).
Abstract: In recent years 3D urban maps have become more common, thus providing complex point clouds that include diverse urban furniture such as pole-like objects. Utility poles detection in urban environment is of particular interest for electric utility companies in order to maintain an updated inventory for better planning and management. The present study develops an automatic method for the detection of utility poles from noisy point cloud data of Guayaquil – Ecuador, where many poles are located next to buildings, or houses are built until the border of the sidewalk getting very close to poles, which increases the difficulty of discriminating poles, walls, columns, fences and building corners.
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2017 |
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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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2016 |
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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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2015 |
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Miguel Realpe, Boris X. Vintimilla, & L. Vlacic. (2015). Towards Fault Tolerant Perception for autonomous vehicles: Local Fusion. In IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM), Siem Reap, 2015. (pp. 253–258).
Abstract: Many robust sensor fusion strategies have been developed in order to reliably detect the surrounding environments of an autonomous vehicle. However, in real situations there is always the possibility that sensors or other components may fail. Thus, internal modules and sensors need to be monitored to ensure their proper function. This paper introduces a general view of a perception architecture designed to detect and classify obstacles in an autonomous vehicle's environment using a fault tolerant framework, whereas elaborates the object detection and local fusion modules proposed in order to achieve the modularity and real-time process required by the system.
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