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					<span style="font-family: Open Sans; font-size: 20px; font-weight: 700; color: #ffffff;">Ian Baldwin <span style="font-weight: 300; color: #bbbbbb;">([email protected])</span></span>
					<span style="margin-top: 5px; font-family: Open Sans; font-size: 14px; color: lightblue;">Ian Baldwin's CV.pdf</span>
					<span style="margin-top: 20px; font-family: Open Sans; font-size: 14px; color: #dddddd;">I don't really understand what Baldwin does (just some description right here)</span>
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						<span style="margin-left: 10px; font-family: Open Sans; font-size: 12px; font-weight: 600; color: #ffffff;">ian.baldy</span>
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						<a style="margin-left: 10px; font-family: Open Sans; font-size: 12px; font-weight: 600; color: #ffffff;" href="https://github.com/iabaldwin">iabaldwin</a>
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				<span style="padding-bottom: 5px; font-family: Open Sans; font-size: 16px; border-bottom: 5px solid;">Posts</span>
				<span style="margin-left: 20px; font-family: Open Sans;  font-size: 16px; color: #666666;">Code and Design</span>
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				<span style="font-family: Open Sans; font-size: 18px; font-weight: 600;">Large-Scale Urban Localisation with a Pushbroom LIDAR</span>
				<span style="margin-top: 10px; font-family: Open Sans; font-size: 14px; color: #666666;">We begin by developing the physical means to make large-scale localisation achievable, and affordable. This takes the form of a stand-alone, rugged sensor payload - incorporating a number of sensing modalities - that can be deployed in either a mapping or localisation role. We then present a new technique for localisation in a prior map using an information-theoretic framework. The core idea is to build a dense retrospective sensor history, which is then matched statistically within a prior map. The underlying idea is to leverage the persistent structure in the environment, and we show that by doing so, it is possible to stay localised over the course of many months and kilometres. The developed system relies on orthogonally-oriented ranging sensors, to infer both velocity and pose. However, operating in a complex, dynamic, setting (like a town centre) can often induce velocity errors, distorting our sensor history and resulting in localisation failure. The insight into dealing with this failure is to learn from sensor context - we learn a place-dependent sensor model and show that doing so is vital to prevent such failures.</span>
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				<span style="font-family: Open Sans; font-size: 18px; font-weight: 600;">Laser-only road-vehicle localization with dual 2D push-broom LIDARS and 3D priors</span>
				<span style="margin-top: 10px; font-family: Open Sans; font-size: 14px; color: #666666;">We demonstrate the viability of using 2D LIDAR data as the sole means for accurate, robust, long-term road-vehicle localization within a prior map in a complex, dynamic real-world setting. We utilize a dual-LIDAR system - one oriented horizontally, in order to infer vehicle linear and rotational velocity, and one declined to capture a dense view of the surrounds - that allows us to estimate both velocity and position within a prior map. We show how probabilistically modelling the noisy local velocity estimates from the horizontal laser feed, fusing these estimates with data from the declined LIDAR to form a dense 3D swathe and matching this swathe statistically within a map will allow for robust, long-term position estimation. We accommodate estimation errors induced by passing vehicles, pedestrians, ground-strike etc., by learning a positional- dependent sensor model - that is, a sensor-model that varies spatially - and show that learning such a model for LIDAR data allows us to deal gracefully with the complexities of real-world data. We validate the concept over more than 9 kilometres of driven distance in and around the town of Woodstock, Oxfordshire.</span>
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				<span style="font-family: Open Sans; font-size: 18px; font-weight: 600;">Road vehicle localization with a 2D push-broom LIDAR and 3D priors</span>
				<span style="margin-top: 10px; font-family: Open Sans; font-size: 14px; color: #666666;">In this paper we describe and demonstrate a method for precisely localizing a road vehicle using a single push-broom 2D laser scanner while leveraging a prior 3D survey. In contrast to conventional scan matching, our laser is oriented downwards, thus causing continual ground strike. Our method exploits this to produce a small 3D swathe of laser data which can be matched statistically within the 3D survey. This swathe generation is predicated upon time varying estimates of vehicle velocity. While in theory this data could be obtained from vehicle speedometers, in reality these instruments are biased and so we also provide a way to estimate this bias from survey data. We show that our low cost system consistently outperforms a high caliber integrated DGPS/IMU system over 26 km of driven path around a test site.</span>
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