Starting August 1, 2021, Prof. Kelly will serve as an Associate Editor for the IEEE Robotics and Automation Society’s Transactions on Robotics. The IEEE T-RO publishes research papers that represent major advances in the state-of-the-art in all areas of robotics and welcomes original papers that report on any combination of theory, design, experimental studies, analysis, algorithms, and integration and application case studies involving all aspects of robotics. T-RO citation statistics from Clarivate analytics, released June 2020, list an impact factor of 6.123 (#2 in robotics).
We’re delighted to announce that STARS Laboratory student Olivier Lamarre will present a public talk on May 12, 2021 as part of the NSERC Science Odyssey Festival! Olivier will describe how rovers navigate safely on Mars – the talk is for all ages, no technical background required! Olivier is a NASA JPL veteran who has spent time there as a visiting researcher and internship student, working on rover path planning. You can watch the free livestream via this link: https://youtu.be/W-x2JEJB-YU
We’re excited to be present two new papers at ICRA 2021 in Xi’an, China! Our first paper describes a learning-based technique to adjust camera gain and exposure parameters to maximize the number of inlier feature matches for visual motion estimation under challenging lighting conditions. We demonstrate successful navigation through road tunnels – a situation where competing algorithms, and built-in auto-exposure and automatic gain adjustment, fail. Grab the preprint here: https://arxiv.org/abs/2102.04341; this work will appear in RA-L and at ICRA.
Our second paper presents a continuous-time approach for extrinsic calibration of cameras and 3D millimetre-wave radar sensors. This research, carried out in partnership with the LAMOR group at the University of Zagreb, enables accurate in-field calibration without the need for radar retroreflectors or specialized visual targets. Grab the preprint here: https://arxiv.org/abs/2103.07505.
Please join us for the presentation sessions at ICRA to learn more!
Congratulations to our alum Valentin Peretroukhin, who received the 2020 Gordon N. Patterson Award for the top PhD dissertation at UTIAS! His thesis, “Learned Improvements to the Visual Egomotion Pipeline,” is available on the laboratory Publications page. Great work Valentin!
Congratulations to Ph.D. student Olivier Lamarre and postdoc Ahmad Bilal Asghar on winning 1st prize at the IROS 2020 Workshop on Planetary Exploration Robots! The associated short abstract describes considerations related to traversability uncertainty for long-duration rover navigation planning on remote planetary surfaces. Great work! Thanks to Moog for sponsoring the award!
Starting January 1, 2021, Prof. Kelly will serve as an Associate Editor for the IEEE Robotics and Automation Society’s Robotics & Automation Magazine (RAM). The magazine has over 12,000 readers and is consistently listed by Thomson’s Journal Citation Reports (JCR) as one of the most highly ranked publication in both the Robotics (#7) and Automation & Control (#15) categories, with an impact factor of 4.250 in 2018 (5-years IF:4.816). The magazine publishes four issues per year: March, June, September and December.
Have you always wondered whether your extrinsic sensor transformation globally minimizes its calibration cost function? With our latest work on certifiable monocular hand-eye calibration, wonder no more! Check out the IEEE MFI 2020 paper – we prove that trajectories satisfying observability requirements lead to convex relaxations that are inherently stable to measurement error. The open source implementation of our method is fast, requires no calibration targets, and works for a wide variety of sensors, including monocular cameras!
Congratulations to lab members Valentin Peretroukhin and Matthew Giamou and to our collaborators, David M. Rosen, W. Nicholas Green, and Nicholas Roy at MIT for winning this year’s RSS Best Student Paper Award! Full details and code for the paper, “A Smooth Representation of SO(3) for Deep Rotation Learning with Uncertainty,” are available here. Great work all!