In this paper we consider long-term navigation using fixed 2D LIDARs. We consider how localization algorithms based on scan-matching - commonly used in indoor environments - are prone to failure when exposed to a challenging real-world outdoor environment. The driving motivation behind this work is to produce a simple, robust system that can be utilized repeatedly over a long period, rather than [...]
LAPS – Localisation using Appearance of Prior Structure: 6-DOF Monocular Camera Localisation using Prior Pointclouds
Abstract— This paper is about pose estimation using monocular cameras with a 3D laser pointcloud as a workspace prior. We have in mind autonomous transport systems in which low cost vehicles equipped with monocular cameras are furnished with preprocessed 3D lidar workspaces surveys. Our inherently cross-modal approach offers robustness to changes in scene lighting and [...]
Abstract—We are concerned with enabling truly large scale autonomous navigation in typical human environments. To this end we describe the acquisition and modeling of large urban spaces from data that reflects human sensory input. Over 181GB of image and inertial data are captured using head- mounted stereo cameras. This data is processed into a relative [...]
Continuous Vehicle Localisation Using Sparse 3D Sensing, Kernelised Renyi Distance and Fast Gauss Transforms
Abstract—This paper is about estimating a smooth, continuous-time trajectory of a vehicle relative to a prior 3D laser map. We pose the estimation problem as that of finding a sequence of Catmull-Rom splines which optimise the Kernelised Rényi Distance (KRD) between the prior map and live measurements from a 3D laser sensor. Our approach treats [...]
In outdoor environments shadows are common. These typically strong visual features cause considerable change in the appearance of a place, and therefore confound vision- based localisation approaches. In this work we describe how to convert a colour image of the scene to a greyscale invariant image where pixel values are a function of underlying [...]
This paper explores the suitability of commonly employed classification methods to action-selection tasks in robotics, and argues that a classifier’s introspective capacity is a vital but as yet largely under-appreciated attribute. As illustration we propose an active learning framework for semantic mapping in mobile robotics and demonstrate it in the context of autonomous driving. In [...]
Autonomous vehicles operating in places like parking lots can leverage of a higher-level understanding of the objects around it. For instance, the knowledge hat there is an upcoming zebra crossing should be taken into account in the vehicle’s current motion plan and speed. Also labelling of parking spots, could be crucial in other tasks as efficient assignment of [...]
The Digital Rail team is led by Akshay Morye and consists of postdoctoral researchers, D.Phil students, support engineers and industrial partners.
The Robotcar flagship team is led by Will Maddern and consists of postdoctoral researchers, D.Phil students, support engineers and industrial partners.
In the context of decision making in robotics, the use of a classification framework which produces scores with inappropriate confidences will ultimately lead to the robot making dangerous decisions. In order to select a framework which will make the best decisions, we should pay careful attention to the ways in which it generates scores. Precision [...]