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Author  |
Angel Sappa; Mohammad Rouhani |
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Title |
Efficient Distance Estimation for Fitting Implicit Quadric Surfaces |
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2009 |
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16th IEEE International Conference on Image Processing |
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3521–3524 |
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This paper presents a novel approach for estimating the shortest Euclidean distance from a given point to the corresponding implicit quadric fitting surface. It first estimates the orthogonal orientation to the surface from the given point; then the shortest distance is directly estimated by intersecting the implicit surface with a line passing through the given point according to the estimated orthogonal orientation. The proposed orthogonal distance estimation is easily obtained without increasing computational complexity; hence it can be used in error minimization surface fitting frameworks. Comparisons of the proposed metric with previous approaches are provided to show both improvements in CPU time as well as in the accuracy of the obtained results. Surfaces fitted by using the proposed geometric distance estimation and state of the art metrics are presented to show the viability of the proposed approach. |
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Cairo, Egypt |
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1522-4880 |
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978-1-4244-5653-6 |
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ICIP |
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ADAS @ adas @ SaR2009 |
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1232 |
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Author |
Angel Sappa; M.A. Garcia |
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Title |
Hierarchical Clustering of 3D Objects and its Application to Minimum Distance Computation |
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2004 |
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IEEE International Conference on Robotics & Automation, 5287–5292, New Orleans, LA (USA), ISBN: 0–7803–8232–3 |
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New Orleans, LA, USA |
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ADAS @ adas @ SaG2004b |
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459 |
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Author |
Angel Sappa; Fadi Dornaika; David Geronimo; Antonio Lopez |
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Title |
Efficient On-Board Stereo Vision Pose Estimation |
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Conference Article |
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Year |
2007 |
Publication |
Computer Aided Systems Theory, Selected paper from |
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4739 |
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1183–1190 |
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This paper presents an efficient technique for real time estimation of on-board stereo vision system pose. The whole process is performed in the Euclidean space and consists of two stages. Initially, a compact representation of the original 3D data points is computed. Then, a RANSAC based least squares approach is used for fitting a plane to the 3D road points. Fast RANSAC fitting is obtained by selecting points according to a probability distribution function that takes into account the density of points at a given depth. Finally, stereo camera position
and orientation—pose—is computed relative to the road plane. The proposed technique is intended to be used on driver assistance systems for applications such as obstacle or pedestrian detection. A real time performance is reached. Experimental results on several environments and comparisons with a previous work are presented. |
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Las Palmas de Gran Canaria (Spain) |
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EUROCAST |
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ADAS @ adas @ SDG2007b |
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916 |
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Angel Sappa; Fadi Dornaika; David Geronimo; Antonio Lopez |
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Title |
Registration-based Moving Object Detection from a Moving Camera |
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2008 |
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IROS2008 2nd Workshop on Perception, Planning and Navigation for Intelligent Vehicles |
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65–69 |
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This paper presents a robust approach for detecting moving objects from on-board stereo vision systems. It relies on a feature point quaternion-based registration, which avoids common problems that appear when computationally expensive iterative-based algorithms are used on dynamic environments. The proposed approach consists of three stages. Initially, feature points are extracted and tracked through consecutive frames. Then, a RANSAC based approach is used for registering
two 3D point sets with known correspondences by means of the quaternion method. Finally, the computed 3D rigid displacement is used to map two consecutive frames into the same coordinate system. Moving objects correspond to those areas with large registration errors. Experimental results, in different scenarios, show the viability of the proposed approach. |
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Nice (France) |
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ADAS @ adas @ SDG2008 |
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1017 |
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Angel Sappa; David Geronimo; Fadi Dornaika; Antonio Lopez |
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Title |
Real Time Vehicle Pose Using On-Board Stereo Vision System |
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Conference Article |
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2006 |
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International Conference on Image Analysis and Recognition |
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ICIAR |
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LNCS 4142 |
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205–216 |
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This paper presents a robust technique for a real time estimation of both camera’s position and orientation—referred as pose. A commercial stereo vision system is used. Unlike previous approaches, it can be used either for urban or highway scenarios. The proposed technique consists of two stages. Initially, a compact 2D representation of the original 3D data points is computed. Then, a RANSAC based least squares approach is used for fitting a plane to the road. At the same time,
relative camera’s position and orientation are computed. The proposed technique is intended to be used on a driving assistance scheme for applications such as obstacle or pedestrian detection. Experimental results on urban environments with different road geometries are presented. |
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ADAS @ adas @ SGD2006b |
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671 |
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Author |
Angel Sappa; Boris X. Vintimilla |
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Title |
Edge Point Linking by Means of Global and Local Schemes |
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Conference Article |
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2006 |
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IEEE Int. Conf. on Signal-Image Technology and Internet-Based Systems, Hammamet, Tunisia, December 2006, pp. 551-560. |
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Hammamet (Tunisia) |
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ADAS @ adas @ SaV2006 |
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722 |
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Andrew Nolan; Daniel Serrano; Aura Hernandez-Sabate; Daniel Ponsa; Antonio Lopez |
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Obstacle mapping module for quadrotors on outdoor Search and Rescue operations |
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Conference Article |
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2013 |
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International Micro Air Vehicle Conference and Flight Competition |
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UAV |
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Obstacle avoidance remains a challenging task for Micro Aerial Vehicles (MAV), due to their limited payload capacity to carry advanced sensors. Unlike larger vehicles, MAV can only carry light weight sensors, for instance a camera, which is our main assumption in this work. We explore passive monocular depth estimation and propose a novel method Position Aided Depth Estimation
(PADE). We analyse PADE performance and compare it against the extensively used Time To Collision (TTC). We evaluate the accuracy, robustness to noise and speed of three Optical Flow (OF) techniques, combined with both depth estimation methods. Our results show PADE is more accurate than TTC at depths between 0-12 meters and is less sensitive to noise. Our findings highlight the potential application of PADE for MAV to perform safe autonomous navigation in
unknown and unstructured environments. |
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Toulouse; France; September 2013 |
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IMAV |
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ADAS; 600.054; 600.057;IAM |
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Admin @ si @ NSH2013 |
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2371 |
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Alexey Dosovitskiy; German Ros; Felipe Codevilla; Antonio Lopez; Vladlen Koltun |
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Title |
CARLA: An Open Urban Driving Simulator |
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Conference Article |
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2017 |
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1st Annual Conference on Robot Learning. Proceedings of Machine Learning |
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78 |
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1-16 |
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Autonomous driving; sensorimotor control; simulation |
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We introduce CARLA, an open-source simulator for autonomous driving research. CARLA has been developed from the ground up to support development, training, and validation of autonomous urban driving systems. In addition to open-source code and protocols, CARLA provides open digital assets (urban layouts, buildings, vehicles) that were created for this purpose and can be used freely. The simulation platform supports flexible specification of sensor suites and environmental conditions. We use CARLA to study the performance of three approaches to autonomous driving: a classic modular pipeline, an endto-end
model trained via imitation learning, and an end-to-end model trained via
reinforcement learning. The approaches are evaluated in controlled scenarios of
increasing difficulty, and their performance is examined via metrics provided by CARLA, illustrating the platform’s utility for autonomous driving research. |
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Mountain View; CA; USA; November 2017 |
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CORL |
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ADAS; 600.085; 600.118 |
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Admin @ si @ DRC2017 |
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2988 |
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Alex Goldhoorn; Arnau Ramisa; Ramon Lopez de Mantaras; Ricardo Toledo |
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Using the Average Landmark Vector Method for Robot Homing |
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2007 |
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Artificial Intelligence Research and Development, Proceedings of the 10th International Conference of the ACIA |
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163 |
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331–338 |
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978–1–58603–798–7 |
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CCIA’07 |
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RV;ADAS |
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Admin @ si @ GRL2007 |
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899 |
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Alejandro Gonzalez Alzate; Sebastian Ramos; David Vazquez; Antonio Lopez; Jaume Amores |
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Spatiotemporal Stacked Sequential Learning for Pedestrian Detection |
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2015 |
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Pattern Recognition and Image Analysis, Proceedings of 7th Iberian Conference , ibPRIA 2015 |
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3-12 |
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SSL; Pedestrian Detection |
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Pedestrian classifiers decide which image windows contain a pedestrian. In practice, such classifiers provide a relatively high response at neighbor windows overlapping a pedestrian, while the responses around potential false positives are expected to be lower. An analogous reasoning applies for image sequences. If there is a pedestrian located within a frame, the same pedestrian is expected to appear close to the same location in neighbor frames. Therefore, such a location has chances of receiving high classification scores during several frames, while false positives are expected to be more spurious. In this paper we propose to exploit such correlations for improving the accuracy of base pedestrian classifiers. In particular, we propose to use two-stage classifiers which not only rely on the image descriptors required by the base classifiers but also on the response of such base classifiers in a given spatiotemporal neighborhood. More specifically, we train pedestrian classifiers using a stacked sequential learning (SSL) paradigm. We use a new pedestrian dataset we have acquired from a car to evaluate our proposal at different frame rates. We also test on a well known dataset: Caltech. The obtained results show that our SSL proposal boosts detection accuracy significantly with a minimal impact on the computational cost. Interestingly, SSL improves more the accuracy at the most dangerous situations, i.e. when a pedestrian is close to the camera. |
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Santiago de Compostela; España; June 2015 |
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IbPRIA |
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ADAS; 600.057; 600.054; 600.076 |
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GRV2015; ADAS @ adas @ GRV2015 |
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2454 |
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