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Geoffrey Pascoe

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3 papers

1 author row

ICRA Conference 2018 Conference Paper

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

Dan Barnes
Will Maddern
Geoffrey Pascoe
Ingmar Posner

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.

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ICRA Conference 2015 Conference Paper

FARLAP: Fast robust localisation using appearance priors

Geoffrey Pascoe
Will Maddern
Alexander D. Stewart
Paul Newman 0001

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 textured 3-dimensional prior mesh using a monocular camera. We first present a system for generating and texturing the prior using a LIDAR scanner and camera. We then describe how we can localise against that prior with a single camera, using an information-theoretic measure of image similarity. This process requires dealing with the distortions induced by a wide-angle camera. We present and justify an interesting approach to this issue in which we distort the prior map into the image rather than vice-versa. Finally we explain how the general purpose computation functionality of a modern GPU is particularly apt for our task, allowing us to run the system in real time. We present results showing centimetre-level localisation accuracy through a city over six kilometres.

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ICRA Conference 2015 Conference Paper

Leveraging experience for large-scale LIDAR localisation in changing cities

Will Maddern
Geoffrey Pascoe
Paul Newman 0001

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-broom 2D LIDAR to a number of prior 3D maps, each of which has been collected during normal driving in different conditions. Local swathes are converted to a combined 2D height and reflectance representation, and we exploit the GPU rendering pipeline to densely sample the localisation cost function to provide robustness and a wide basin of convergence. Prior maps are incrementally built into an experience-based framework from multiple traversals of the same environment, capturing changes in environment structure and appearance over time. The LIDAR localisation solutions from each prior map are fused with vehicle odometry in a probabilistic framework to provide a single pose solution suitable for automated driving. Using this framework we demonstrate real-time centimetre-level localisation using LIDAR data collected in a dynamic city environment over a period of a year.

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