Benchmarking 6DOF Outdoor Visual Localization in Changing Conditions
This work addresses the need for reliable localization in autonomous vehicles and augmented reality by providing a benchmark to assess robustness to environmental changes, though it is incremental as it focuses on dataset creation and evaluation rather than new methods.
The paper tackles the problem of robust 6DOF visual localization under varying outdoor conditions like day-night and weather changes by introducing the first benchmark datasets for this purpose, evaluating state-of-the-art methods to show that long-term localization remains challenging.
Visual localization enables autonomous vehicles to navigate in their surroundings and augmented reality applications to link virtual to real worlds. Practical visual localization approaches need to be robust to a wide variety of viewing condition, including day-night changes, as well as weather and seasonal variations, while providing highly accurate 6 degree-of-freedom (6DOF) camera pose estimates. In this paper, we introduce the first benchmark datasets specifically designed for analyzing the impact of such factors on visual localization. Using carefully created ground truth poses for query images taken under a wide variety of conditions, we evaluate the impact of various factors on 6DOF camera pose estimation accuracy through extensive experiments with state-of-the-art localization approaches. Based on our results, we draw conclusions about the difficulty of different conditions, showing that long-term localization is far from solved, and propose promising avenues for future work, including sequence-based localization approaches and the need for better local features. Our benchmark is available at visuallocalization.net.