Predictive Data Acquisition for Lifelong Visual Teach, Repeat and Learn

Abstract

Nowadays, robots can operate in environments which are not tailored for them. This allows their deployments in changing and human-populated environments, which recent advances in machine learning methods enabled. The efficiency of these methods is largely determined by the quality of their training data. An up-to-date and well-balanced training dataset is paramount for achieving robust robot operation. To achieve long-term operation, the robot has to deal with perpetual environmental changes, forcing it to keep its models up-to-date. We present an exploration method allowing a mobile robot to gather high-quality data to update its models both while performing its duties and when idle, maximizing effectivity. The robot evaluates the quality of the data gathered in the past and based on that, it creates preferences which influence how often these locations are visited. This exploration method was integrated with a self-supervised visual teach-and-repeat pipeline. We show the precision and robustness of visual-based navigation to improve when using machine-learned models trained by our exploration method. Our research resulted in a robotic navigation system that can not only annotate its training data but also ensure that its training dataset is balanced and up-to-date.