Chi Hay Tong

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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 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)
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 describe the development and evaluation of a core algorithmic component for robust robotic planetary surface mapping. In particular, we consider the issue of outlier measurements when utilizing both odometry and sparse features for laser scan alignment. Due to the heterogeneity of the measurements and the relative scarcity of distinct(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 a method for obtaining Visual Odometry (VO) estimates using a scanning laser rangefinder. Though common VO implementations utilize stereo camera imagery, cameras are dependent on ambient light. In contrast, actively-illuminated sensors such as laser rangefinders work in a variety of lighting conditions, including full darkness. We(More)