Where am I? This is one of the most critical questions that any intelligent system should
answer to decide whether it navigates to a previously visited area. This problem has long …

Autonomous Vehicles (AV) are becoming more capable of navigating in complex
environments with dynamic and changing conditions. A key component that enables these …

In this paper, a novel pipeline for loop-closure detection is proposed. We base our work on a
bag of binary feature words and we produce a description vector capable of characterizing a …

In this paper, we propose a novel appearance-based approach for topological mapping
based on a hierarchical decomposition of the environment. In our map, images with similar …

Autonomous path-following systems based on the Teach and Repeat paradigm allow robots
to traverse extensive networks of manually driven paths using on-board sensors. These …

Abstract The use of Convolutional Neural Networks (CNNs) in image analysis and
recognition paved the way for long-term visual place recognition. The transferable power of …

Our work builds upon Visual Teach & Repeat 2 (VT&R2): a vision-in-the-loop autonomous
navigation system that enables the rapid construction of route networks, safely built through …

As robots continue to create long-term maps, the amount of information that they need to
handle increases over time. In terms of place recognition, this implies that the number of …

Appearance-based techniques for simultaneous localization and mapping (SLAM) have
been highly successful in assisting robot-motion estimation; however, these vision-based …

Visual navigation is a key enabling technology for autonomous mobile vehicles. The ability
to provide large‐scale, long‐term navigation using low‐cost, low‐power vision sensors is …