We present a challenging new dataset for autonomous driving: the Oxford RobotCar Dataset. Over the period of May 2014 to December 2015 we traversed a route through central Oxford twice a week on average using the Oxford RobotCar platform, an autonomous Nissan LEAF. This resulted in over 1000km of recorded driving with almost 20 million images collected from 6 cameras mounted to the vehicle, along with LIDAR, GPS and INS ground truth. Data was collected in all weather conditions, including heavy rain, night, direct sunlight and snow. Road and building works over the period of a year significantly changed sections of the route from the beginning to the end of data collection. By frequently traversing the same route over the period of a year we enable research investigating long-term localisation and mapping for autonomous vehicles in real-world, dynamic urban environments. The full dataset is available for download at: http://robotcar-dataset.robots.ox.ac.uk

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.

No abstract

We propose a direct monocular SLAM algorithm based on the Normalised Information Distance (NID) metric. In contrast to current state-of-the-art direct methods based on photometric error minimisation, our information-theoretic NID metric provides robustness to appearance variation due to lighting, weather and structural changes in the scene. We demonstrate successful localisation and mapping across changes in lighting with a synthetic indoor scene, and across changes in weather (direct sun, rain, snow) using real-world data collected from a vehicle-mounted camera. Our approach runs in real-time on a consumer GPU using OpenGL, and provides comparable localisation accuracy to state-of-the-art photometric methods but significantly outperforms both direct and feature-based methods in robustness to appearance changes.

No abstract

No abstract

We present a unified approach to describing certain types of collective decision making in swarm robotics that bridges from a microscopic individual-based description to aggregate properties. Our approach encompasses robot swarm experiments, microscopic and probabilistic macroscopic-discrete simulations as well as an analytic mathematical model. Following up on previous work, we identify the symmetry parameter, a measure of the progress of the swarm towards a decision, as a fundamental integrated swarm property and formulate its time evolution as a continuous-time Markov process. Contrary to previous work, which justified this approach only empirically and a posteriori, we justify it from first principles and derive hard limits on the parameter regime in which it is applicable.

Real-time visual localisation is a key technology enabling mobile location applications [7], virtual and augmented reality [1] and robotics [3]. The recent availablity of low-cost GPU hardware and GPGPU programming has enabled a new class of 'direct' visual localisation methods that make use of every pixel from an input image for tracking and matching [6], in contrast to traditional feature-based methods that only use a subset of the input image. The additional information available to direct methods localising against a dense 3D map increases robustness against typical failure modes for feature-based methods, such as motion blur and viewpoint change [2]. In this paper we present a visual localisation system which utilises the entropy-based Normalised Information Distance (NID) metric for image registration.For computational reasons, existing direct methods generally minimise a cost function based on photometric error on a per-pixel basis (e.g. [6]), which assumes both the live image and the reference map are embedded in the same space. Equation (1) defines such a metric based on the sum of squared differences between corresponding pixels in a reference image (I r ) and a synthetic image (I s ).whereT is a pixel location within the image.Although photoconsistency is efficient to compute and find derivatives for (in order to use in an optimisation framework), as mentioned in [6] it suffers from a number of limitations. Principally, it requires I s provide a photorealistic rendering of the scene, such that the resulting synthetic image matches the reference image I r on a pixel-by-pixel basis. A true match under significant appearance changes would require modelling of the material and illumination properties of the real-world environment, along with the response of the sensor. This limitation restricts photoconsistency to applications involving frame-to-frame tracking, where the synthetic image I s can be derived from a warping of the previous reference image I r , or applications in small indoor environments with controlled illumination where the scene does not change over time [6].In this paper we instead make use of the Normalised Information Distance (NID) metric, given by Equation (2) [4].where H(I r , I s ) and MI(I r , I s ) are the joint entropy and mutual information of the two images respectively, defined as follows:where H (I r ) is defined similarly to H (I s ). p s and p r,s are the marginal and joint discrete distributions of the images I r and I s , represented by nbin discrete histograms where a and b are individual bin indices.As NID is not a function of the actual values of the pixels in the image, but of their distribution, NID provides robustness to illumination change and sensor modality. Unlike mutual information, NID is a true metric, which satisfies the triangle inequality, and is normalised over the number of pixels used in the calculation, thus allowing comparisons between image pairs with differing amounts of overlap.In order to use NID as a cost function in a gradient-based optimisatio...