Improving Robot Localisation by Ignoring Visual Distraction from IROS 2021. PDF. Full Presentation. We introduce the idea of Neural Blindness, which gives an agent the ability to completely ignore objects or classes that are deemed distractors. We render a neural network completely incapable of representing specific chosen classes in its latent space, allowing and agent to focus on what is important for a given task, and demonstrate how this can be used to improve localisation.
MDN-VO: Estimating Visual Odometry with Confidence from IROS 2021. PDF. Full Presentation. We propose a deep learning-based VO model to efficiently estimate 6-DoF poses, as well as a confidence model for these estimates. We employ a Mixture Density Network (MDN) which estimates camera motion as a mixture of Gaussians, based on the extracted spatio-temporal representations.
Birds-Eye-View (BEV) from Monocular Images from ICRA 2021. PDF. We show how monocular images can be used to learn instantaneous Birds-Eye-View (BEV) estimation of a scene. We also show how a better state estimation of the world can be achieved by incorporating temporal information. Our model learns a representation from monocular video through factorised 3D convolutions and uses this to estimate a BEV occupancy grid of the final frame.
Reinforcement Learning for Navigation from ICRA 2021. PDF. We propose a new approach to navigation, where it is treated as a multi-task learning problem. This enables the robot to learn to behave differently in visual navigation tasks for different environments while also learning shared expertise across environments.
CNN-Based Markov Localisation and Odometry Propagation from ICRA 2021. PDF. We present a novel CNN-based localisation approach that can leverage modern deep learning hardware by implementing a grid-based Markov localisation approach directly on the GPU. We create a hybrid Convolutional Neural Network (CNN) that can perform image based localisation and odometry-based likelihood propagation within a single neural network.
SeDAR - Localisation without LiDAR from ICRA2018. PDF. How does a person work out their location using a floorplan? It is probably safe to say that we do not explicitly measure depths to every visible surface and try to match them against different pose estimates in the floorplan. And yet, this is exactly how most robotic scan-matching algorithms operate. Humans do the exact opposite. Instead of depth, we use high level semantic cues. In this work, we use this insight to present a global localisation approach that relies solely on the semantic labels present in the floorplan and extracted from RGB images.
Taking the Scenic Route to 3D from ICCV2017. PDF. We use live robots and simulated drones to demonstrate our Scenic Route Planner, which selects paths which maximise information gain, both in terms of total map coverage and reconstruction accuracy.
A project made by one of my students (Celyn Walters) allows control of a Baxter Robot using a VR headset, ROS and Unity.