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Deep Graph Laplacian Hashing for Image Retrieval Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Dense Frame-to-Model SLAM with an RGB-D Camera

Authors : Xiaodan Ye, Jianing Li, Lianghao Wang, Dongxiao Li, Ming Zhang

Published in: Advances in Multimedia Information Processing – PCM 2017

Publisher: Springer International Publishing

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Abstract

In this paper, a dense frame-to-model Simultaneous Localization And Mapping (SLAM) with an RGB-D camera is proposed, which achieves a more accurate trajectory in contrast to traditional frame-to-model methods. In the frontend, dense photometric information and geometric information are combined to perform a more robust tracking. In the backend, we add volume to loop closure detection to reject false loop. A novel volume-camera pose graph is proposed to effectively reduce drift. Experimental results on some RGB-D SLAM datasets show a reduction of global trajectory error by 18.60% in comparison to Kinituous, 84.43% in comparison to Kinfu.

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previous chapter MCTD: Motion-Coordinate-Time Descriptor for 3D Skeleton-Based Action Recognition
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Metadata
Title
Dense Frame-to-Model SLAM with an RGB-D Camera
Authors
Xiaodan Ye
Jianing Li
Lianghao Wang
Dongxiao Li
Ming Zhang
Copyright Year
2018
Book
Advances in Multimedia Information Processing – PCM 2017

Print ISBN: 978-3-319-77379-7

Electronic ISBN: 978-3-319-77380-3

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-77380-3_56