11 accepted papers
Recently there has been growing interest in the use of aerial and satellite map data for autonomous vehicles, primarily due to its potential for significant cost reduction and enhanced scalability. Despite the advantages, aerial data also comes with challenges such as a sensor-modality gap and a vie
For autonomous vehicles to be able to operate successfully they need to be aware of other vehicles with sufficient time to make safe, stable plans. Given the possible closing speeds between two vehicles, this necessitates the ability to accurately detect distant vehicles. Many current image-based ob…
We present a method of improving visual place recognition and metric localisation under very strong appearance change. We learn an invertable generator that can transform the conditions of images, e.g. from day to night, summer to winter etc. This image transforming filter is explicitly designed to…
Visual localization enables autonomous vehicles to navigate in their surroundings and augmented reality applications to link virtual to real worlds. Practical visual localization approaches need to be robust to a wide variety of viewing condition, including day-night changes, as well as weather and…
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…
This paper presents a weakly-supervised learning system for real-time road marking detection using images of complex urban environments obtained from a monocular camera. We avoid expensive manual labelling by exploiting additional sensor modalities to generate large quantities of annotated images in…
We present a weakly-supervised approach to segmenting proposed drivable paths in images with the goal of autonomous driving in complex urban environments. Using recorded routes from a data collection vehicle, our proposed method generates vast quantities of labelled images containing proposed paths…
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, we…
Real-time 3D perception is critical for localisation, mapping, path planning and obstacle avoidance for mobile robots and autonomous vehicles. For outdoor operation in real-world environments, 3D perception is often provided by sparse 3D LIDAR scanners, which provide accurate but low-density depth m…
This paper is concerned with large-scale localisation at city scales with monocular cameras. Our primary motivation lies with the development of autonomous road vehicles - an application domain in which low-cost sensing is particularly important. Here we present a method for localising against a tex…
Recent successful approaches to autonomous vehicle localisation and navigation typically involve 3D LIDAR scanners and a static, curated 3D map, both of which are expensive to acquire and maintain. In this paper we propose an experience-based approach to matching a local 3D swathe built using a push…