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

Authors: Yali Zheng, Peipei Luo, Shinan Chen, Jiasheng Hao, Hong Cheng

Abstract:

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.

Keywords: Indoor navigation, low light, RGB-D camera, vision based.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1129950

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References:


[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.
[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
[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.
[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.
[5] Endres, F., Hess, J., Engelhard, N., Sturm, J., Cremers, D., Burgard, W., An Evaluation of the RGB-D SLAM System, ICRA, 2012.
[6] Engel J, Schops T, Cremers D., LSD-SLAM: Large-scale direct monocular SLAM, ECCV, 2014.
[7] Salas-Moreno, R,, Newcombe, R., Strasdat, H., et al., Slam++: Simultaneous localisation and mapping at the level of objects, CVPR, 2013.
[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.
[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.
[10] Labb M., Michaud F., Memory management for real-time appearance-based loop closure detection, IEEE Intelligent Robots and Systems (IROS), 2011.
[11] Rublee, E., Rabaud, V., Konolige, K., et al., ORB: an efficient alternative to SIFT or SURF, IEEE International Conference on Computer Vision, 2011.
[12] Zhong, Y., Intrinsic shape signatures: A shape descriptor for 3D object recognition, IEEE International Conference on Computer Vision Workshops, 2009.
[13] Glvez-Lpez, D., Tardos, J. D., Bags of binary words for fast place recognition in image sequences(J). IEEE Transactions on Robotics, 2012, 28(5): 1188-1197.