CityLearn: Diverse Real-World Environments for Sample-Efficient Navigation Policy Learning

Abstract

<div>Visual navigation tasks in real-world environments often require both self-motion and place recognition feedback. While deep reinforcement learning has shown success in solving these perception and decision-making problems in an end-to-end manner, these algorithms require large amounts of experience to learn navigation policies from high-dimensional data, which is generally impractical for real robots due to sample complexity. In this paper, we address these problems with two main contributions. We first leverage place recognition and deep learning techniques combined with goal destination feedback to generate compact, bimodal image representations that can then be used to effectively learn control policies from a small amount of experience. Second, we present an interactive framework, CityLearn, that enables for the first time training and deployment of navigation algorithms across city-sized, realistic environments with extreme visual appearance changes. CityLearn features more than 10 benchmark datasets, often used in visual place recognition and autonomous driving research, including over 100 recorded traversals across 60 cities around the world. We evaluate our approach on two CityLearn environments, training our navigation policy on a single traversal. Results show our method can be over 2 orders of magnitude faster than when using raw images, and can also generalize across extreme visual changes including day to night and summer to winter transitions.</div>

A Survey on Deep Visual Place Recognition

“…Thirdly, the rise of mobile robots operating in the open world, e.g., selfdriving cars, and the inherent challenge of their long term autonomy. Pertaining to the last point, recognizing places by vision is regarded as a key component for localization and navigation, being used for loop-closure in SLAM algorithms in GPS denied environments as well as an input to learn navigation policies [1] under different conditions. Remarkably, the development of visual localization in robotics is also paving the way for new applications of VPR, such as assistive technologies for people with visual impairments [2].…”