Humans can robustly localize themselves without a map after they get lost
following prominent visual cues or landmarks. In this work, we aim at endowing
autonomous agents the same ability. Such ability is important in robotics
applications yet very challenging when an agent is exposed to partially
calibrated environments, where camera images with accurate 6 Degree-of-Freedom
pose labels only cover part of the scene. To address the above challenge, we
explore using Reinforcement Learning to search for a policy to generate
intelligent motions so as to actively localize the agent given visual
information in partially calibrated environments. Our core contribution is to
formulate the active visual localization problem as a Partially Observable
Markov Decision Process and propose an algorithmic framework based on Deep
Reinforcement Learning to solve it. We further propose an indoor scene dataset
ACR-6, which consists of both synthetic and real data and simulates challenging
scenarios for active visual localization. We benchmark our algorithm against
handcrafted baselines for localization and demonstrate that our approach
significantly outperforms them on localization success rate.Comment: https://www.youtube.com/watch?v=DIH-GbytCPM&feature=youtu.b