Proceedings ArticleDOI
Robust visual localization in changing lighting conditions
Pyojin Kim,Brian Coltin,Oleg Alexandrov,H. Jin Kim +3 more
- pp 5447-5452
TLDR
An illumination-robust visual localization algorithm for Astrobee, a free-flying robot designed to autonomously navigate on the International Space Station, is presented and it is discovered that maps built in darker conditions can also be effective in bright conditions, but the reverse is not true.Abstract:
We present an illumination-robust visual localization algorithm for Astrobee, a free-flying robot designed to autonomously navigate on the International Space Station (ISS). Astrobee localizes with a monocular camera and a pre-built sparse map composed of natural visual features. Astrobee must perform tasks not only during the day, but also at night when the ISS lights are dimmed. However, the localization performance degrades when the observed lighting conditions differ from the conditions when the sparse map was built. We investigate and quantify the effect of lighting variations on visual feature-based localization systems, and discover that maps built in darker conditions can also be effective in bright conditions, but the reverse is not true. We extend Astrobee's localization algorithm to make it more robust to changing-light environments on the ISS by automatically recognizing the current illumination level, and selecting an appropriate map and camera exposure time. We extensively evaluate the proposed algorithm through experiments on Astrobee.read more
Citations
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Journal Article
SeqSLAM : visual route-based navigation for sunny summer days and stormy winter nights
Michael Milford,Gordon Wyeth +1 more
TL;DR: A new approach to visual navigation under changing conditions dubbed SeqSLAM, which removes the need for global matching performance by the vision front-end - instead it must only pick the best match within any short sequence of images.
Journal ArticleDOI
Robust Light-Weight Magnetic-Based Door Event Detection with Smartphones
TL;DR: An improved and robust door event detection framework based on a majority-voting model is proposed to fuse multiple-dimensional sensing data from non-magnetic built-in sensors, and the enhanced LMDD based on the fusion of heterogeneous sensors can achieve a much higher doorevent detection accuracy.
Posted Content
Sparse Depth Enhanced Direct Thermal-infrared SLAM Beyond the Visible Spectrum
Young-Sik Shin,Ayoung Kim +1 more
TL;DR: A method to use sparse depth measurement for 6-DOF motion estimation via direct tracking under 14-bit raw measurement from the thermal camera enhanced by sparse depth measurements from light detection ranging (LiDAR) is proposed.
Proceedings ArticleDOI
Visual Appearance Analysis of Forest Scenes for Monocular SLAM
James Garforth,Barbara Webb +1 more
TL;DR: It is found that SLAM systems struggle with all but the most straightforward forest terrain and key attributes which distinguish forest scenes from “classic” urban datasets are identified, offering an insight into what makes forests harder to map and open the way for targeted improvements.
Proceedings ArticleDOI
Monocular Visual Odometry using Learned Repeatability and Description
TL;DR: A monocular VO system leveraging learned repeatability and description is presented in a hybrid scheme, where the camera pose is initially tracked on the predicted repeatability maps in a direct manner and then refined with the patch-wise 3D-2D association.
References
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Journal ArticleDOI
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Proceedings ArticleDOI
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