David joined ORI in May 2019 and works with Dr Ioannis Havoutis.
In the last decade, systems utilising camera and lasers have been remarkably successful increasing our expectations for what robotics might achieve in the decade to come. Our robots now need to see further, not only operating in environments where humans can operate, but also in environments where humans cannot! To this end radar is a [...]
Our recent work on analysing a set of permutation invariant neural network architectures is probably on the theoretical end of the spectrum of the type of work we do at the A2I lab. Nevertheless it is equally exciting as it has concrete implications for real-world robotics such as working with point clouds from Lidars. [...]
Learning to See the Wood for the Trees: Deep Laser Localization in Urban and Natural Environments on a CPU Georgi Tinchev, Adrian Penate-Sanchez, Maurice Fallon IEEE Robotics and Automation Letters/IEEE International Conference on Robotics and Automation (RA-L/ICRA) 2019 [arXiv] [Slides (TBA)] Figure 1. PCA visualization of the feature space after training our model. Each sample [...]
Driven to Distraction: Self-Supervised Distractor Learning for Robust Monocular Visual Odometry in Urban Environments Abstract – We present a self-supervised approach to ignoring “distractors” in camera images for the purposes of robustly estimating vehicle motion in cluttered urban environments. We leverage offline multi-session mapping approaches to automatically generate a per-pixel ephemerality mask and depth map for each [...]
Oliwier joined ORI in 2018 and is supervised by Maurice Fallon.
Hierarchical Attentive Recurrent Tracking Abstract – Class-agnostic object tracking is particularly difficult in cluttered environments as target specific discriminative models cannot be learned a priori. Inspired by how the human visual cortex employs spatial attention and separate “where” and “what” processing pathways to actively suppress irrelevant visual features, this work develops a hierarchical attentive recurrent model [...]
Abstract - Training robots for operation in the real world is a complex, time consuming and potentially expensive task. Despite significant success of reinforcement learning in games and simulations, research in real robot applications has not been able to match similar progress. While sample complexity can be reduced by training policies in simulation, these can [...]
Addressing Appearance Change in Outdoor Robotics with Adversarial Domain Adaptation Abstract – Appearance changes due to weather and seasonal conditions represent a strong impediment to the robust implementation of machine learning systems in outdoor robotics. While the model is optimised for the training domain it will deliver degraded performance in application domains that underlie distributional shifts [...]
Learn from Experience: Probabilistic Prediction of Perception Performance to Avoid Failure Abstract –Despite the significant advances in machine learning and perception over the past few decades, perception algorithms can still be unreliable when deployed in challenging, time-varying environments. When these systems are used for autonomous decision-making, such as in self-driving vehicles, the impact of their [...]