Visual Search Based Indoor Localization in Low Light via RGB-D Camera

<!DOCTYPE html> <html lang="en" dir="ltr"> <head>  <script async src="https://www.googletagmanager.com/gtag/js?id=G-P63WKM1TM1"></script> <script> window.dataLayer = window.dataLayer || []; function gtag(){dataLayer.push(arguments);} gtag('js', new Date()); gtag('config', 'G-P63WKM1TM1'); </script>  <script type="text/javascript" > (function(m,e,t,r,i,k,a){m[i]=m[i]||function(){(m[i].a=m[i].a||[]).push(arguments)}; m[i].l=1*new Date(); for (var j = 0; j < document.scripts.length; j++) {if (document.scripts[j].src === r) { return; }} k=e.createElement(t),a=e.getElementsByTagName(t)[0],k.async=1,k.src=r,a.parentNode.insertBefore(k,a)}) (window, document, "script", "https://mc.yandex.ru/metrika/tag.js", "ym"); ym(55165297, "init", { clickmap:false, trackLinks:true, accurateTrackBounce:true, webvisor:false }); </script> <noscript><div><img src="https://mc.yandex.ru/watch/55165297" style="position:absolute; left:-9999px;" alt="" /></div></noscript>    <title>Visual Search Based Indoor Localization in Low Light via RGB-D Camera</title> <meta name="description" content="Visual Search Based Indoor Localization in Low Light via RGB-D Camera"> <meta name="keywords" content="Indoor navigation, low light, RGB-D camera, vision based."> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <meta name="citation_title" content="Visual Search Based Indoor Localization in Low Light via RGB-D Camera"> <meta name="citation_author" content="Yali Zheng"> <meta name="citation_author" content="Peipei Luo"> <meta name="citation_author" content="Shinan Chen"> <meta name="citation_author" content="Jiasheng Hao"> <meta name="citation_author" content="Hong Cheng"> <meta name="citation_publication_date" content="2017/03/01"> <meta name="citation_journal_title" content="International Journal of Computer and Information Engineering"> <meta name="citation_volume" content="11"> <meta name="citation_issue" 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class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value=""> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 33093</div> </div> </div> </div> <div class="card publication-listing mt-3 mb-3"> <h5 class="card-header" style="font-size:.9rem">Visual Search Based Indoor Localization in Low Light via RGB-D Camera</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Yali%20Zheng">Yali Zheng</a>, <a href="https://publications.waset.org/search?q=Peipei%20Luo"> Peipei Luo</a>, <a href="https://publications.waset.org/search?q=Shinan%20Chen"> Shinan Chen</a>, <a href="https://publications.waset.org/search?q=Jiasheng%20Hao"> Jiasheng Hao</a>, <a href="https://publications.waset.org/search?q=Hong%20Cheng"> Hong Cheng</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Most of traditional visual indoor navigation algorithms and methods only consider the localization in ordinary daytime, while we focus on the indoor re-localization in low light in the paper. As RGB images are degraded in low light, less discriminative infrared and depth image pairs are taken, as the input, by RGB-D cameras, the most similar candidates, as the output, are searched from databases which is built in the bag-of-word framework. Epipolar constraints can be used to relocalize the query infrared and depth image sequence. We evaluate our method in two datasets captured by Kinect2. The results demonstrate very promising re-localization results for indoor navigation system in low light environments. <iframe src="https://publications.waset.org/10006850.pdf" style="width:100%; height:400px;" frameborder="0"></iframe> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Indoor%20navigation" title="Indoor navigation">Indoor navigation</a>, <a href="https://publications.waset.org/search?q=low%20light" title=" low light"> low light</a>, <a href="https://publications.waset.org/search?q=RGB-D%20camera" title=" RGB-D camera"> RGB-D camera</a>, <a href="https://publications.waset.org/search?q=vision%0D%0Abased." title=" vision based."> vision based.</a> </p> <p class="card-text"><strong>Digital Object Identifier (DOI):</strong> <a href="https://doi.org/10.5281/zenodo.1129950" target="_blank">doi.org/10.5281/zenodo.1129950</a> </p> <a href="https://publications.waset.org/10006850/visual-search-based-indoor-localization-in-low-light-via-rgb-d-camera" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10006850/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10006850/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10006850/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10006850/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10006850/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10006850/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10006850/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10006850/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10006850/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10006850/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10006850.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1676</span> </span> <p class="card-text"><strong>References:</strong></p> <br>[1] Lowry, S., Sunderhauf, N., Newman, P., Leonard, J. J., Cox, D., Corke, P., Milford, M. J., Visual Place Recognition: A Survey, IEEE Transactions on Robotics, 2015, 31(1): 1–19. <br>[2] Williams B., Cummins M., Neira J., Newman P., Reid I., Tardos J. D., A comparison of loop closing techniques in monocular SLAM, Robotics and Autonomous Systems, vol. 57, no. 12, pp. 1188C1197, 2009 <br>[3] Lee D, Kim H, Myung H., 2D image feature-based real-time RGB-D 3D SLAM, Robot Intelligence Technology and Applications, 2012: 485-492. <br>[4] Mur-Artal R, Montiel J. M. M., Tardos J D., ORB-SLAM: a versatile and accurate monocular SLAM system, IEEE Transactions on Robotics, 2015, 31(5): 1147-1163. <br>[5] Endres, F., Hess, J., Engelhard, N., Sturm, J., Cremers, D., Burgard, W., An Evaluation of the RGB-D SLAM System, ICRA, 2012. <br>[6] Engel J, Schops T, Cremers D., LSD-SLAM: Large-scale direct monocular SLAM, ECCV, 2014. <br>[7] Salas-Moreno, R,, Newcombe, R., Strasdat, H., et al., Slam++: Simultaneous localisation and mapping at the level of objects, CVPR, 2013. <br>[8] Labbe M, Michaud F., Appearance-based loop closure detection for online large-scale and long-term operation, IEEE Transactions on Robotics, 2013, 29(3): 734-745. <br>[9] Labbe M, Michaud F., Online global loop closure detection for large-scale multi-session graph-based slam, IEEE Intelligent Robots and Systems (IROS), 2014. <br>[10] Labb M., Michaud F., Memory management for real-time appearance-based loop closure detection, IEEE Intelligent Robots and Systems (IROS), 2011. <br>[11] Rublee, E., Rabaud, V., Konolige, K., et al., ORB: an efficient alternative to SIFT or SURF, IEEE International Conference on Computer Vision, 2011. <br>[12] Zhong, Y., Intrinsic shape signatures: A shape descriptor for 3D object recognition, IEEE International Conference on Computer Vision Workshops, 2009. <br>[13] Glvez-Lpez, D., Tardos, J. D., Bags of binary words for fast place recognition in image sequences(J). 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Visual Search Based Indoor Localization in Low Light via RGB-D Camera