Driven to Distraction: Self-Supervised Distractor Learning for Robust Monocular Visual Odometry in Urban Environments

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 input image, which we use to train a deep convolutional network. At run-time we use the predicted ephemerality and depth as an input to a monocular visual odometry (VO) pipeline, using either sparse features or dense photometric matching. Our approach yields metric-scale VO using only a single camera and can recover the correct egomotion even when 90% of the image is obscured by dynamic, independently moving objects. We evaluate our robust VO methods on more than 400km of driving from the Oxford RobotCar Dataset and demonstrate reduced odometry drift and significantly improved egomotion estimation in the presence of large moving vehicles in urban traffic.Comment: International Conference on Robotics and Automation (ICRA), 2018. Video summary: http://youtu.be/ebIrBn_nc-

Turning on the heat: ecological response to simulated warming in the sea

Significant warming has been observed in every ocean, yet our ability to predict the consequences of oceanic warming on marine biodiversity remains poor. Experiments have been severely limited because, until now, it has not been possible to manipulate seawater temperature in a consistent manner across a range of marine habitats. We constructed a "hot-plate'' system to directly examine ecological responses to elevated seawater temperature in a subtidal marine system. The substratum available for colonisation and overlying seawater boundary layer were warmed for 36 days, which resulted in greater biomass of marine organisms and a doubling of space coverage by a dominant colonial ascidian. The "hot-plate'' system will facilitate complex manipulations of temperature and multiple stressors in the field to provide valuable information on the response of individuals, populations and communities to environmental change in any aquatic habitat

Under the Radar: Learning to Predict Robust Keypoints for Odometry Estimation and Metric Localisation in Radar

This paper presents a self-supervised framework for learning to detect robust keypoints for odometry estimation and metric localisation in radar. By embedding a differentiable point-based motion estimator inside our architecture, we learn keypoint locations, scores and descriptors from localisation error alone. This approach avoids imposing any assumption on what makes a robust keypoint and crucially allows them to be optimised for our application. Furthermore the architecture is sensor agnostic and can be applied to most modalities. We run experiments on 280km of real world driving from the Oxford Radar RobotCar Dataset and improve on the state-of-the-art in point-based radar odometry, reducing errors by up to 45% whilst running an order of magnitude faster, simultaneously solving metric loop closures. Combining these outputs, we provide a framework capable of full mapping and localisation with radar in urban environments.Comment: Video summary: https://youtu.be/L-PO7nxWpJ

Specific detection of interferon regulatory factor 5 (IRF5): A case of antibody inequality

Interferon regulatory factor 5 (IRF5) is a member of the IRF family of transcription factors. IRF5 was first identified and characterized as a transcriptional regulator of type I interferon expression after virus infection. In addition to its critical role(s) in the regulation and development of host immunity, subsequent studies revealed important roles for IRF5 in autoimmunity, cancer, obesity, pain, cardiovascular disease, and metabolism. Based on these important disease-related findings, a large number of commercial antibodies have become available to study the expression and function of IRF5. Here we validate a number of these antibodies for the detection of IRF5 by immunoblot, flow cytometry, and immunofluorescence or immunohistochemistry using well-established positive and negative controls. Somewhat surprising, the majority of commercial antibodies tested were unable to specifically recognize human or mouse IRF5. We present data on antibodies that do specifically recognize human or mouse IRF5 in a particular application. These findings reiterate the importance of proper controls and molecular weight standards for the analysis of protein expression. Given that dysregulated IRF5 expression has been implicated in the pathogenesis of numerous diseases, including autoimmune and cancer, results indicate that caution should be used in the evaluation and interpretation of IRF5 expression analysis

RSL-Net: Localising in Satellite Images From a Radar on the Ground

This paper is about localising a vehicle in an overhead image using FMCW radar mounted on a ground vehicle. FMCW radar offers extraordinary promise and efficacy for vehicle localisation. It is impervious to all weather types and lighting conditions. However the complexity of the interactions between millimetre radar wave and the physical environment makes it a challenging domain. Infrastructure-free large-scale radar-based localisation is in its infancy. Typically here a map is built and suitable techniques, compatible with the nature of sensor, are brought to bear. In this work we eschew the need for a radar-based map; instead we simply use an overhead image -- a resource readily available everywhere. This paper introduces a method that not only naturally deals with the complexity of the signal type but does so in the context of cross modal processing.Comment: Accepted to IEEE Robotics and Automation Letters (RA-L

Kidnapped Radar: Topological Radar Localisation using Rotationally-Invariant Metric Learning

This paper presents a system for robust, large-scale topological localisation using Frequency-Modulated Continuous-Wave (FMCW) scanning radar. We learn a metric space for embedding polar radar scans using CNN and NetVLAD architectures traditionally applied to the visual domain. However, we tailor the feature extraction for more suitability to the polar nature of radar scan formation using cylindrical convolutions, anti-aliasing blurring, and azimuth-wise max-pooling; all in order to bolster the rotational invariance. The enforced metric space is then used to encode a reference trajectory, serving as a map, which is queried for nearest neighbours (NNs) for recognition of places at run-time. We demonstrate the performance of our topological localisation system over the course of many repeat forays using the largest radar-focused mobile autonomy dataset released to date, totalling 280 km of urban driving, a small portion of which we also use to learn the weights of the modified architecture. As this work represents a novel application for FMCW radar, we analyse the utility of the proposed method via a comprehensive set of metrics which provide insight into the efficacy when used in a realistic system, showing improved performance over the root architecture even in the face of random rotational perturbation.Comment: submitted to the 2020 International Conference on Robotics and Automation (ICRA