Wednesday, 4 November 2015
Building on our previous work, we demonstrate how it is possible to improve flight control of a MAV that experiences aerodynamic disturbances caused by objects on its path. Predictions based on low resolution depth images taken at a distance are incorporated into the flight control loop on the throttle channel as this is adjusted to target undisrupted level flight. We demonstrate that a statistically significant improvement (p << 0:001) is possible for some common obstacles such as boxes and steps, compared to using conventional feedback-only control. Our approach and results are encouraging toward more autonomous MAV exploration strategies.
- John Bartholomew, Andrew Calway, Walterio Mayol-Cuevas, Improving MAV Control by Predicting Aerodynamic Effects of Obstacles. IEEE/RSJ International Conference on Intelligent Robots and Systems. September 2015. [PDF]
- Luis Contreras Toledo, Walterio Mayol-Cuevas, Trajectory-Driven Point Cloud Compression Techniques for Visual SLAM. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). October 2015. [PDF]
Tuesday, 1 September 2015
How to obtain attention for an eyewear computer without a gaze tracker? We developed this method for Google Glass.
This paper concerns with the evaluation of methods for the estimation of both temporal and spatial visual attention using a head-worn inertial measurement unit (IMU). Aimed at tasks where there is a wearer-object interaction, we estimate the when and the where the wearer is interested in. We evaluate various methods on a new egocentric dataset from 8 volunteers and compare our results with those achievable with a commercial gaze tracker used as ground-truth. Our approach is primarily geared for sensor-minimal EyeWear computing.
- Teesid Leelasawassuk, Dima Damen, Walterio W Mayol-Cuevas, Estimating Visual Attention from a Head Mounted IMU. ISWC '15 Proceedings of the 2015 ACM International Symposium on Wearable Computers. ISBN 978-1-4503-3578-2, pp. 147–150. September 2015. PDF, 2045 Kbytes. External information
Wednesday, 6 May 2015
Two papers related to RGBD mapping from a nice collaboration with Daniel Gutierrez and Josechu Guerrero from the University of Zaragoza. Both to be presented at ICRA 2015. One of them nominated for Awards:
- D. Gutiérrez-Gómez, W. Mayol-Cuevas, J.J. Guerrero. "Inverse Depth for Accurate Photometric and Geometric Error Minimisation in RGB-D Dense Visual Odometry", In IEEE International Conference on Robotics and Automation (ICRA), 2015. Nominated for Best Robotic Vision Paper Award. [pdf][video][code available]
- D. Gutiérrez-Gómez, W. Mayol-Cuevas, J.J. Guerrero. "What Should I Landmark? Entropy of Normals in Depth Juts for Place Recognition in Changing Environments Using RGB-D Data", In IEEE International Conference on Robotics and Automation (ICRA), 2015.[pdf]
Monday, 1 September 2014
We have been working for 2.5 years on prototypes (and since 2006 in the concept!) on what we think is a new extended type of robot. Handheld robots have the shape of tools and are intended to have cognition and action while cooperating with people. This video is from our first prototype back in November 2013. We are also offering details of its construction and 3D CAD models at www.handheldrobotics.org . We are currently developing a new prototype and more on this soon. Austin Gregg-Smith is sponsored by the James Dyson Foundation.
- Austin Gregg-Smith and Walterio Mayol. The Design and Evaluation of a Cooperative Handheld Robot. IEEE International Conference on Robotics and Automation (ICRA). Seattle, Washington, USA. May 25th-30th, 2015. [PDF] Nominated for Best Cognitive Robotics Paper Award.
Saturday, 9 August 2014
Dealing with real transparent objects for AR is challenging due to their lack of texture and visual features as well as the drastic changes in appearance as the background, illumination and camera pose change. In this work, we explore the use of a learning approach for classifying transparent objects from multiple images with the aim of both discovering such objects and building a 3D reconstruction to support convincing augmentations. We extract, classify and group small image patches using a fast graph-based segmentation and employ a probabilistic formulation for aggregating spatially consistent glass regions. We demonstrate our approach via analysis of the performance of glass region detection and example 3D reconstructions that allow virtual objects to interact with them.
From our paper: Alan Francisco Torres-Gomez, Walterio Mayol-Cuevas, Recognition and reconstruction of transparent objects for Augmented Reality. ISMAR 2014. PDF available at here.
Friday, 1 August 2014
Discovering Task Relevant Objects, their Usage and Providing Video Guides from Multi-User Egocentric Video
- Damen, Dima and Leelasawassuk, Teesid and Haines, Osian and Calway, Andrew and Mayol-Cuevas, Walterio (2014). You-Do, I-Learn: Discovering Task Relevant Objects and their Modes of Interaction from Multi-User Egocentric Video. British Machine Vision Conference (BMVC), Nottingham, UK. [pdf]
- Damen, Dima and Haines, Osian and Leelasawassuk, Teesid and Calway, Andrew and Mayol-Cuevas, Walterio (2014). Multi-user egocentric Online System for Unsupervised Assistance on Object Usage. ECCV Workshop on Assistive Computer Vision and Robotics (ACVR), Zurich, Switzerland. [preprint]
Wednesday, 23 April 2014
With the current "easiness" with which 3D maps are now possible to be constructed, this work in a way aims to enhance maps with information that is beyond purely geometric. We have also closed the control loop so we correct for the deviation in anticipation, but that is for another paper.
- John Bartholomew, Andrew Calway and Walterio Mayol-Cuevas, Learning to Predict Obstacle Aerodynamics from Depth Images for Micro Air Vehicles by , IEEE ICRA 2014. [PDF]
Saturday, 12 October 2013
How to get a 3D model of something one looks at without any clicks or even feedback to the user?
From our paper T. Leelasawassuk and W.W. Mayol-Cuevas. 3D from Looking: Using Wearable Gaze Tracking for Hands-Free and Feedback-Free Object Modelling. ISWC 2013.
Monday, 1 July 2013
Image from our Advanced Robotics Journal paper
Real-time 3D simultaneous localization and map-building for a dynamic walking humanoid robot
S Yoon, S Hyung, M Lee, KS Roh, SH Ahn, A Gee, P Bunnun, A Calway, & WW Mayol-Cuevas,
Advanced Robotics, published online on May 1st, 2013.
Friday, 28 June 2013
In this work we present our fast (50Hz) relocalisation method based on simple visual descriptors plus a 3D geometrical test for a system performing visual 6-D relocalisation at every single frame and in real time. Continuous relocalisation is useful in re-exploration of scenes or for loop-closure in earnest. Our experiments suggest the feasibility of this novel approach that benefits from depth camera data, with a relocalisation performance of 73% while running on a single core onboard a moving platform over trajectory segments of about 120m. The system also reduces in 95% the memory footprint compared to a system using conventional SIFT-like descriptors.
- J. Martinez-Carranza, Walterio Mayol-Cuevas. Real-Time Continuous 6D Relocalisation for Depth Cameras. Workshop on Multi VIew Geometry in RObotics (MVIGRO), in conjunction with Robotics Science and Systems RSS. Berlin, Germany. June, 2013. PDF
- J. Martinez Carranza, A. Calway, W. Mayol-Cuevas, Enhancing 6D visual relocalisation with depth cameras. International Conference on Intelligent Robots and Systems IROS. November 2013.
Wednesday, 1 May 2013
These videos show work we been doing with our partners at Blue Bear for onboard visual mapping for MAVs. These are based on visual odometry mapping for working over large areas and build maps onboard the MAV using an asus xtion-pro RGBD camera mounted on the vehicle. One of the videos show autoretrieval of the vehicle where a human pilot first flies the vehicle through the space and then the map is used for relocalisation using the map built on the way back. The other video is on a nuclear reactor installation. These are works we been doing for a while on uses of our methods for industrial inspection.
Monday, 25 March 2013
March 2013 New Robotics MSc. I redesigned our joint MSc in Robotics which is now aimed to support students from various backgrounds in Engineering, Physics and Maths. I am looking forward to supervise MSc project here. Have a look at the programme here. The application deadline is August 31st.
Thursday, 1 November 2012
Click image for video
- Pished Bunnun, Dima Damen, Andrew Calway, Walterio Mayol-Cuevas, Integrating 3D Object Detection, Modelling and Tracking on a Mobile Phone. International Symposium on Mixed and Augmented Reality (ISMAR). November 2012. PDF.
Sunday, 7 October 2012
How does a UAV can decide where is best to land and what to expect if landing on a particular material? Here we develop a framework to predict the landing behaviour of a Micro Air Vehicle (MAV) from the visual appearance of the landing surface. We approach this problem by learning a mapping from visual texture observed from an onboard camera to the landing behaviour on a set of sample materials. In this case we exemplify our framework by predicting the yaw angle of the MAV after landing.
- John Bartholomew, Andrew Calway, Walterio Mayol-Cuevas, Predicting Micro Air Vehicle Landing Behaviour from Visual Texture . IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). October 2012. PDF.
We developed an integrated system for personal workspace monitoring based around an RGB-D sensor. The approach is egocentric, facilitating full flexibility, and operates in real-time, providing object detection and recognition, and 3D trajectory estimation whilst the user undertakes tasks in the workspace. A prototype on-body system developed in the context of work-flow analysis for industrial manipulation and assembly tasks is described. We evaluated on two tasks with multiple users, and results indicate that the method is effective, giving good accuracy performance.
Monday, 3 September 2012
Relocalisation is about finding out where the camera is in translation and rotation (6D) when it visits the space for the first time after a map has been created, or if gets lost during tracking due to occlusion. This is also known as the "kidnapped robot" problem in Robotics and appears frequently in SLAM at the loopclosing stage. Here, we develop a fast relocalisation method for RGB-D cameras that operates in workplaces where low texture and some occlusion can be present. Videos are available here.
- Andrew P. Gee, Walterio Mayol-Cuevas, 6D Relocalisation for RGBD Cameras Using Synthetic View Regression. Proceedings of the British Machine Vision Conference (BMVC). September 2012. PDF.