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http://dx.doi.org/10.17703/IJACT.2021.9.4.302

Visual Positioning System based on Voxel Labeling using Object Simultaneous Localization And Mapping

Jung, Tae-Won (Department of Immersive Content Convergence, Kwangwoon University)
Kim, In-Seon (Department of Smart System, Kwangwoon University)
Jung, Kye-Dong (Ingenium College of Liberal Arts, Kwangwoon University)

Publication Information

International Journal of Advanced Culture Technology / v.9, no.4, 2021 , pp. 302-306 More about this Journal

Abstract

Indoor localization is one of the basic elements of Location-Based Service, such as indoor navigation, location-based precision marketing, spatial recognition of robotics, augmented reality, and mixed reality. We propose a Voxel Labeling-based visual positioning system using object simultaneous localization and mapping (SLAM). Our method is a method of determining a location through single image 3D cuboid object detection and object SLAM for indoor navigation, then mapping to create an indoor map, addressing it with voxels, and matching with a defined space. First, high-quality cuboids are created from sampling 2D bounding boxes and vanishing points for single image object detection. And after jointly optimizing the poses of cameras, objects, and points, it is a Visual Positioning System (VPS) through matching with the pose information of the object in the voxel database. Our method provided the spatial information needed to the user with improved location accuracy and direction estimation.

Keywords

Visual Positioning System; Simultaneous Localization and Mapping; Augmented Reality; Deep Learning;

Citations & Related Records

Reference

1	Jianxiong Xiao, Bryan Russell and Antonio Torralba, "Localizing 3D cuboids in single-view images," In Advances in neural information processing systems (NIPS), pp. 746-754, 2012.
2	Xiaozhi Chen, Kaustav Kundu, Ziyu Zhang, Huimin Ma, Sanja Fidler and Raquel Urtasun, "Monocular 3D object detection for autonomous driving," In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2147-2156, 2016.
3	S. Yang and S. Scherer, "Cubeslam: Monocular 3-d object slam," IEEE Transactions on Robotics, vol. 35, no. 4, pp. 925-938, 2019. DOI
4	Raul Mur-Artal, J.M.M. Montiel and Juan D. Tardos, "ORB-SLAM: a versatile and accurate monocular SLAM system," IEEE Transactions on Robotics, 31(5):1147-1163, 2015. DOI
5	Renato F. Salas-Moreno, Richard A. Newcombe, Hauke Strasdat, Paul H.J. Kelly and Andrew J. Davison, "SLAM++: Simultaneous localisation and mapping at the level of objects," In IEEE Conference on Computer Vision and Pattern Recognition, pp. 1352-1359, 2013.
6	Dorian Galvez-Lopez, Marta Salas, Juan D. Tardos and J.M.M. Montiel, "Real-time monocular object SLAM," Robotics and Autonomous Systems, 75:435-449, 2016. DOI
7	Nan Yang, Rui Wang, Jorg Stuckler and Daniel Cremers, "Leveraging deep depth prediction for monocular direct sparse odometry," In European Conference on Computer Vision, pp. 835-852. Springer, 2018.
8	Florian Chabot, Mohamed Chaouch, Jaonary Rabarisoa, Celine Teuliere and Thierry Chateau, "Deep MANTA: A coarse-to-fine many-task network for joint 2d and 3D vehicle analysis from monocular image," In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2040-2049, 2017.
9	Jakob Engel, Vladlen Koltun and Daniel Cremers, "Direct sparse odometry," IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017.
10	Wadim Kehl, Fabian Manhardt, Federico Tombari, Slobodan Ilic and Nassir Navab, "SSD-6D: Making rgb-based 3D detection and 6d pose estimation great again," In IEEE International Conference on Computer Vision, 2017.