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—In this paper, we revisit batch state estimation through the lens of Gaussian process (GP) regression. We consider continuous-discrete estimation problems wherein a trajectory is viewed as a one-dimensional GP, with time as the independent variable. Our continuous-time prior can be defined by any linear, time-varying stochastic differential equation driven(More)
This paper proposes a computationally efficient approach to detecting objects natively in 3D point clouds using convolutional neural networks (CNNs). In particular, this is achieved by leveraging a feature-centric voting scheme to implement novel convolutional layers which explicitly exploit the sparsity encountered in the input. To this end, we examine the(More)
In this paper, we present an infrastructure-based ground-truth localization system suitable for deployment in large worksite environments. In particular, the system is low-cost, simple-to-deploy, and is able to provide full six-degree-of-freedom relative localization for three-dimensional laser scanners with centimetre-level accuracy in translation, and(More)
In this paper, we revisit batch state estimation through the lens of Gaussian process (GP) regression. We consider continuous-discrete estimation problems wherein a trajectory is viewed as a one-dimensional GP, with time as the independent variable. Our continuous-time prior can be defined by any nonlinear, time-varying stochastic differential equation(More)
This paper is about building maps which not only contain the traditional information useful for localising - such as point features - but also embeds a spatial model of expected localiser performance. This often overlooked second-order information provides vital context when it comes to map use and planning. Our motivation here is to improve the performance(More)
In this paper, we present Gaussian Process Gauss-Newton (GPGN), an algorithm for non-parametric, continuous-time, nonlinear, batch state estimation. This work adapts the methods of Gaussian Process regression to the problem of batch state estimation by using the Gauss-Newton method. In particular, we formulate the estimation problem with a continuous-time(More)