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A bio-inspired robot with visual perception of Affordances

A bio-inspired robot with visual perception of Affordances
We present a visual robot whose associated neural controller develops a realistic perception of affordances. The controller uses known insect brain principles; particularly the time stabilized sparse code communication between the Antennal Lobe and the Mushroom Body
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 We present a visual robot whose associated neural controller develops a realistic perception of affordances. The controller uses known insect brain principles; particularly the time stabilized sparse code communication between the Antennal Lobe and the Mushroom Body

Researches: 

A Supervised Approach to Electric Tower Detection and Classification for Power Line Inspection

Publication
A Supervised Approach to Electric Tower Detection and Classification for Power Line Inspection
This paper proposes a supervised learning approach for solving the tower detection and classification problem. The first classifier is used for background-foreground segmentation, and the second multi-class MLP is used for classifying within 4 different types of electric towers. A thorough evaluation of the tower detection and classification approach has been carried out on image data from real inspections tasks with different types of towers and backgrounds, that show that a learning-based approach is a promising technique for power line inspection.
Congresses name: 

IEEE World Congress on Computational Intelligence (IEEE WCCI 2014)

Place: 

Beijing - China

Date: 
2014 July 6-11

 

Inspection of power line infrastructures must be periodically conducted by electric companies in order to ensure reliable electric power distribution.

 

A Ground-Truth Benchmark Video Dataset for the Validation of Visual-based Sense-and-Avoid systems

Publication
A Ground-Truth Benchmark Video Dataset for the Validation of Visual-based Sense-and-Avoid systems
Congresses name: 

2014 International Conference on Unmanned Aircraft Systems (ICUAS'14)

Place: 

Orlando, Florida, USA

Date: 
27 - 30 May, 2014

Online Learning-based Robust Visual Tracking for Autonomous Landing of Unmanned Aerial Vehicles

Publication
Online Learning-based Robust Visual Tracking for Autonomous Landing of Unmanned Aerial Vehicles
Congresses name: 

2014 International Conference on Unmanned Aircraft Systems (ICUAS'14)

Place: 

Orlando, Florida, USA

Date: 
27 - 30 May, 2014

Towards Autonomous Detection and Tracking of Electric Towers for Aerial Power Line Inspection

Publication
Towards Autonomous Detection and Tracking of Electric Towers for Aerial Power Line Inspection
This paper presents an approach towards autonomous aerial power line inspection. In particular, the presented work focuses on real-time autonomous detection, localization and tracking of electric towers. A strategy which combines classic computer vision and machine learning techniques, is proposed. A generalized detection and localization approach is presented, where a two-class multilayer perceptron (MLP) neural network was trained for Tower-Background classification. This MLP is applied over sliding windows for each camera frame until a tower is detected. The detection of a tower triggers the tracker. A hierarchical tracking methodology, especially designed for tracking towers in real-time, is presented. This methodology is based on the Hierarchical Multi-Parametric and Multi-Resolution Inverse Compositional Algorithm [1], and is proposed to be used for tracking and maintaining the tower in the field of view (FOV). The proposed strategy, which is the combination of the tower detector and the tracker, is evaluated on videos from several real manned helicopter inspections. Overall, the results show that the proposed strategy performs very well at detecting and tracking various types of electric towers in diverse environmental settings.
Congresses name: 

International Conference on Unmanned Aircraft Systems

Place: 

Orlando, Florida, USA

Date: 
May 27-30, 2014

Abstract:

Towards Autonomous Air-to-Air Refuelling for UAVs using visual information

Publication
Towards Autonomous Air-to-Air Refuelling for UAVs using visual information
For the purposes of achieving Autonomous Air-to-Air Refuelling (AAAR) in Unmanned Aerial Vehicles (UAVs), this paper presents the use of a visual tracking algorithm based on direct methods and image registration techniques, with the aim of solving the drogue tracking problem in order to obtain vision-based relative position estimations of the aircrafts for the probe and drogue refuelling method. Proposed vision-based AAAR approaches to date have explored the use of features (such as corners, painted marks, or LEDs) to detect and estimate the relative motion of either the receiver or the tanker aircraft, with the drawback that sometimes this requires the installation of specific hardware on-board. Conversely, the strategy we propose to use does not require the installation of additional hardware on-board. The strategy is based on a hierarchical implementation of an image registration technique: the Inverse Compositional Image Alignment ICIA. Real images and real flight hardware (probe and drogue) are used to test the algorithm using a robotic testbed that simulates the motion of the aircrafts (the tanker and the receiver) during the refuelling task. Results show that the tracking algorithm is robust to fast motion, changes in appearance, and situations where part of the drogue is occluded or is outside of the field of view of the camera. Additionally, results show that robust position estimations at real-time frame rates are obtained, proving that the technique is fast enough to form the basis for automated aerial refuelling sensing capabilities.
Congresses name: 

IEEE International Conference on Robotics and Automation (ICRA), 2013

Place: 

Karlsruhe, Germany

Date: 
6-10 May 2013

Abstract:

A Vision-Based Strategy for Autonomous Aerial Refueling Tasks

Publication
A Vision-Based Strategy for Autonomous Aerial Refueling Tasks
Autonomous aerial refueling is a key enabling technology for both manned and unmanned aircraft where extended flight duration or range are required. The results presented within this paper offer one potential vision-based sensing solution, together with a unique test environment. A hierarchical visual tracking algorithm based on direct methods is proposed and developed for the purposes of tracking a drogue during the capture stage of autonomous aerial refueling, and of estimating its 3D position. Intended to be applied in real time to a video stream from a single monocular camera mounted on the receiver aircraft, the algorithm is shown to be highly robust, and capable of tracking large, rapid drogue motions within the frame of reference. The proposed strategy has been tested using a complex robotic testbed and with actual flight hardware consisting of a full size probe and drogue. Results show that the vision tracking algorithm can detect and track the drogue at real-time frame rates of more than thirty frames per second, obtaining a robust position estimation even with strong motions and multiple occlusions of the drogue.
Journal: 
Robotics and Autonomous Systems

ISSN: 0921-8890

Paper reference: 
http://dx.doi.org/10.1016/j.robot.2013.02.006
Publication Date: 
August 2013

Abstract:

A Hierarchical Tracking Strategy for Vision-Based Applications On-Board UAVs

Publication
A Hierarchical Tracking Strategy for Vision-Based Applications On-Board UAVs
In this paper, we apply a hierarchical tracking strategy of planar objects (or that can be assumed to be planar) that is based on direct methods for vision-based applications on-board UAVs. The use of this tracking strategy allows to achieve the tasks at real-time frame rates and to overcome problems posed by the challenging conditions of the tasks: e.g. constant vibrations, fast 3D changes, or limited capacity on-board. The vast majority of approaches make use of feature-based methods to track objects. Nonetheless, in this paper we show that although some of these feature-based solutions are faster, direct methods can be more robust under fast 3D motions (fast changes in position), some changes in appearance, constant vibrations (without requiring any specific hardware or software for video stabilization), and situations in which part of the object to track is outside of the field of view of the camera. The performance of the proposed tracking strategy on-board UAVs is evaluated with images from real-flight tests using manually-generated ground truth information, accurate position estimation using a Vicon system, and also with simulated data from a simulation environment. Results show that the hierarchical tracking strategy performs better than well-known feature-based algorithms and well-known configurations of direct methods, and that its performance is robust enough for vision-in-the-loop tasks, e.g. for vision-based landing tasks.
Journal: 
Journal of Intelligent and Robotics Systems

ISSN: 0921-0296 (print version) ISSN: 1573-0409 (electronic version)

Paper reference: 
DOI: 10.1007/s10846-013-9814-x
Publication Date: 
December 2013,

Abstract:

HMPMR Strategy for Real-Time Tracking in Aerial Images, Using Direct Methods

Publication
HMPMR Strategy for Real-Time Tracking in Aerial Images, Using Direct Methods
The vast majority of approaches make use of features to track objects. In this paper we address the tracking problem with a tracking-by-registration strategy based on direct methods. We proposes a hierarchical strategy in terms of image resolution and number of parameters estimated in each resolution, that allows direct methods to be applied in demanding realtime visual tracking applications. We have called this strategy the Hierarchical Multi-Parametric and Multi- Resolution strategy (HMPMR). The Inverse Composition Image Alignment Algorithm (ICIA) is used as image registration technique and it is extended to an HMPMR-ICIA. The propose strategy is tested with di↵erent datasets; and also with image data from real flight tests using an Unmanned Aerial Vehicle (UAV), where the requirements of direct methods are easily unsatisfied (e.g. vehicle vibrations). Results show that by using an HMPMR approach, it is possible to cope with the efficiency problem and with the small motion constraint of direct methods, conducting the tracking task at real-time frame rates and obtaining a performance that is comparable to, or even better, than the one obtained with the other algorithms that were analyzed.
Journal: 
Machine Vision and Applications

0932-8092

Publication Date: 
2014

Abstract:

Robust Real-time Vision-based Aircraft Tracking From Unmanned Aerial Vehicles

Publication
Robust Real-time Vision-based Aircraft Tracking From Unmanned Aerial Vehicles
Congresses name: 

2014 IEEE International Conference on Robotics and Automation (ICRA 2014)

Place: 

Hong Kong Convention and Exhibition Center, Hong Kong, China

Date: 
May 31 - June 7, 2014
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