8 research outputs found
Benchmarking 6DOF Outdoor Visual Localization in Changing Conditions
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.Comment: Accepted to CVPR 2018 as a spotligh
To Learn or Not to Learn: Visual Localization from Essential Matrices
Visual localization is the problem of estimating a camera within a scene and
a key component in computer vision applications such as self-driving cars and
Mixed Reality. State-of-the-art approaches for accurate visual localization use
scene-specific representations, resulting in the overhead of constructing these
models when applying the techniques to new scenes. Recently, deep
learning-based approaches based on relative pose estimation have been proposed,
carrying the promise of easily adapting to new scenes. However, it has been
shown such approaches are currently significantly less accurate than
state-of-the-art approaches. In this paper, we are interested in analyzing this
behavior. To this end, we propose a novel framework for visual localization
from relative poses. Using a classical feature-based approach within this
framework, we show state-of-the-art performance. Replacing the classical
approach with learned alternatives at various levels, we then identify the
reasons for why deep learned approaches do not perform well. Based on our
analysis, we make recommendations for future work.Comment: Accepted to ICRA 202
Long-Term Visual Localization Revisited
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 conditions, including day-night changes, as well as weather and seasonal variations, while providing highly accurate six degree-of-freedom (6DOF) camera pose estimates. In this paper, we extend three publicly available datasets containing images captured under a wide variety of viewing conditions, but lacking camera pose information, with ground truth pose information, making evaluation of the impact of various factors on 6DOF camera pose estimation accuracy possible. We also discuss the performance of state-of-the-art localization approaches on these datasets. Additionally, we release around half of the poses for all conditions, and keep the remaining half private as a test set, in the hopes that this will stimulate research on long-term visual localization, learned local image features, and related research areas. Our datasets are available at visuallocalization.net, where we are also hosting a benchmarking server for automatic evaluation of results on the test set. The presented state-of-the-art results are to a large degree based on submissions to our server
Towards Robust Visual Localization in Challenging Conditions
Visual localization is a fundamental problem in computer vision, with a multitude of applications in robotics, augmented reality and structure-from-motion. The basic problem is to, based on one or more images, figure out the position and orientation of the camera which captured these images relative to some model of the environment. Current visual localization approaches typically work well when the images to be localized are captured under similar conditions compared to those captured during mapping. However, when the environment exhibits large changes in visual appearance, due to e.g. variations in weather, seasons, day-night or viewpoint, the traditional pipelines break down. The reason is that the local image features used are based on low-level pixel-intensity information, which is not invariant to these transformations: when the environment changes, this will cause a different set of keypoints to be detected, and their descriptors will be different, making the long-term visual localization problem a challenging one. In this thesis, four papers are included, which present work towards solving the problem of long-term visual localization. Three of the articles present ideas for how semantic information may be included to aid in the localization process: one approach relies only on the semantic information for visual localization, another shows how the semantics can be used to detect outlier feature correspondences, while the third presents a sequential localization algorithm which relies on the consistency of the reprojection of a semantic model, instead of traditional features. The final article is a benchmark paper, where we present three new benchmark datasets aimed at evaluating localization algorithms in the context of long-term visual localization