4 accepted papers
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 spa…
This paper proposes an appearance-based approach to estimating localisation performance in the context of visual teach and repeat. Specifically, it aims to estimate the likely corridor around a taught trajectory within which a vision-based localisation system is still able to localise itself. In con
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…
This paper explores the idea of reducing a robot's energy consumption while following a trajectory by turning off the main localisation subsystem and switching to a lower-powered, less accurate odometry source at appropriate times. This applies to scenarios where the robot is permitted to deviate fr…