Miguel Angel Sotelo
University of Alcalá
265 Papers
1.2K Citations
Miguel Angel Sotelo is an academic researcher from University of Alcalá. The author has contributed to research in topics: Computer science & Object detection. The author has an hindex of 35, co-authored 235 publications. Previous affiliations of Miguel Angel Sotelo include University of Alabama.
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Papers
A Hybrid Vision-Map Method for Urban Road Detection
TL;DR: A hybrid vision-map system is presented to solve the road detection problem in urban scenarios by fusing stereo vision with digital maps by detecting road appearance and road limits such as lane markings or curbs.
Robust-FusionNet: Deep Multimodal Sensor Fusion for 3-D Object Detection Under Severe Weather Conditions
TL;DR: A new multimodal sensor fusion object detection network that effectively overcomes the shortcomings caused by the camera and LiDAR distortions in severe weather conditions and achieves robust environment perception.
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"XPFCP": An Extended Particle Filter for Tracking Multiple and Dynamic Objects in Complex Environments
Marta Marrón,Miguel Angel Sotelo,Juan C. García,D. Fernandez,Daniel Pizarro +4 more
- 20 Jun 2005
TL;DR: The work presented in this paper explores a new solution for tracking multiple and dynamic objects in complex environments that includes a clustering procedure that increases the robustness of the probabilistic process to adapt itself on-line to the variable number of clusters.
Vehicle fuzzy driving based on DGPS and vision
Miguel Angel Sotelo,S. Alcalde,J. Reviejo,Jose E. Naranjo,Ricardo Garcia,T. de Pedro,Carlos González +6 more
- 25 Jul 2001
TL;DR: This document presents a fuzzy control application in the unmanned driving field that is essentially DGPS-based providing obstacles detection and avoidance by means of artificial vision in a reactive manner.
21
•Journal Article
WiFi Localization System Using Fuzzy Rule-Based Classification
TL;DR: In this paper, the authors proposed the use of fuzzy rule-based classification in order to obtain the robot position during the estimation stage, after a short training stage where only a few significant WiFi measures are needed.
20