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Large-scale localization datasets in crowded indoor spaces
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| Donghwan Lee, Soohyun Ryu, Suyong Yeon, Yonghan Lee, Deokhwa Kim, Cheolho Han, Yohann Cabon, Philippe Weinzaepfel, Nicolas Guerin, Gabriela Csurka, Martin Humenberger |
| Computer Vision and Pattern Recognition (CVPR 2021), virtual event, 16-25 June, 2021 |
| Paper |
| Datasets |
Estimating the precise location of a camera using visual localization enables interesting applications such as augmented reality or navigation. This is particularly useful in indoor environments where other localization technologies, such as GNSS, fail. Indoor spaces impose interesting challenges on visual localization algorithms: occlusions due to people, textureless surfaces, viewpoint changes might cause large changes in appearance due to close structures, low light, repetitive textures, just to name a few. In order to develop robust algorithms that are able to cope with these challenges, a few indoor datasets were proposed. However, these datasets are either comparably small or do only cover a subset of the mentioned challenges. In this paper, we present 5 new indoor datasets for visual localization in challenging real-world environments. They were captured in a large shopping mall and a large metro station in Seoul, South Korea using a dedicated mapping platform consisting of 10 cameras and 2 laser scanners. In order to compute ground truth camera poses, we developed a robust LiDAR SLAM which provides initial poses that are then refined using a new structure from motion framework especially designed for this purpose. We also present a benchmark of modern visual localization algorithms on these challenging datasets. The datasets will be publicly released to foster future research.
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VISION
The research we conduct on expressive visual representations is applicable to visual search, object detection, image classification and the automatic extraction of 3D human poses and shapes that can be used for human behavior understanding and prediction, human-robot interaction or even avatar animation. We also extract 3D information from images that can be used for intelligent robot navigation, augmented reality and the 3D reconstruction of objects, buildings or even entire cities.
Our work covers the spectrum from unsupervised to supervised approaches, and from very deep architectures to very compact ones. We’re excited about the promise of big data to bring big performance gains to our algorithms but also passionate about the challenge of working in data-scarce and low-power scenarios.
Furthermore, we believe that a modern computer vision system needs to be able to continuously adapt itself to its environment and to improve itself via lifelong learning. Our driving goal is to use our research to deliver embodied intelligence to our users in robotics, autonomous driving, via phone cameras and any other visual means to reach people wherever they may be.
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