People who design, use, and are affected by autonomous artificially intelligent agents want
to be able to trust such agents—that is, to know that these agents will perform correctly, to …

As deep learning continues to dominate all state-of-the-art computer vision tasks, it is
increasingly becoming an essential building block for robotic perception. This raises …

Despite significant advances in machine learning and perception over the past few decades,
perception algorithms can still be unreliable when deployed in challenging time-varying …

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 …

In dynamic and cramped industrial environments, achieving reliable Visual Teach and
Repeat (VT&R) with a single-camera is challenging. In this work, we develop a robust …

This paper explores the idea of predicting the likely performance of a robot's perception
system based on past experience in the same workspace. In particular, we propose to build …

As long as autonomous systems have existed, we have created methods by which we can
have an assurance that the systems are working as expected; this process has largely been …

Despite the push toward fast, reliable vision-based multirotor flight, most vision-based
navigation systems still rely on controllers that are perception-agnostic. Given that these …

Reliably detecting obstacles and identifying traversable areas is a key challenge in mobile
robotics. For redundancy, information from multiple sensors is often fused. In this work we …

Modern perception systems are notoriously complex, featuring dozens of interacting
parameters that must be tuned to achieve good performance. Conventional tuning …