Using Image Sequences for Long-Term Visual Localization

Estimating the pose of a camera in a known scene, i.e., visual localization, is a core task for applications such as self-driving cars. In many scenarios, image sequences are available and existing work on combining single-image localization with odometry offers to unlock their potential for improving localization performance. Still, the largest part of the literature focuses on single-image localization and ignores the availability of sequence data. The goal of this paper is to demonstrate the potential of image sequences in challenging scenarios, e.g., under day-night or seasonal changes. Combining ideas from the literature, we describe a sequence-based localization pipeline that combines odometry with both a coarse and a fine localization module. Experiments on long-term localization datasets show that combining single-image global localization against a prebuilt map with a visual odometry / SLAM pipeline improves performance to a level where the extended CMU Seasons dataset can be considered solved. We show that SIFT features can perform on par with modern state-of-the-art features in our framework, despite being much weaker and a magnitude faster to compute. Our code is publicly available at github.com/rulllars

Beyond Controlled Environments: 3D Camera Re-Localization in Changing Indoor Scenes

Long-term camera re-localization is an important task with numerous computer vision and robotics applications. Whilst various outdoor benchmarks exist that target lighting, weather and seasonal changes, far less attention has been paid to appearance changes that occur indoors. This has led to a mismatch between popular indoor benchmarks, which focus on static scenes, and indoor environments that are of interest for many real-world applications. In this paper, we adapt 3RScan - a recently introduced indoor RGB-D dataset designed for object instance re-localization - to create RIO10, a new long-term camera re-localization benchmark focused on indoor scenes. We propose new metrics for evaluating camera re-localization and explore how state-of-the-art camera re-localizers perform according to these metrics. We also examine in detail how different types of scene change affect the performance of different methods, based on novel ways of detecting such changes in a given RGB-D frame. Our results clearly show that long-term indoor re-localization is an unsolved problem. Our benchmark and tools are publicly available at waldjohannau.github.io/RIO10Comment: ECCV 2020, project website https://waldjohannau.github.io/RIO1

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