Paper Info
Title
A submap joining algorithm for 3D reconstruction using an RGB-D camera based on point and plane features
Abstract
In standard point-based methods, the depth measurements of the point features suffer from noise, which will lead to incorrect global structure of the environment. This paper presents a submap joining based SLAM with an RGB-D camera by introducing planes as well as points as features.This work is consisted of two steps: submap building and submap joining. Several adjacent keyframes, with the corresponding small patches, visual feature points, and planes observed from these keyframes, are used to build a submap. We fuse the submaps into a global map in a sequential fashion, such that, the global structure is recovered gradually through plane feature associations and optimization. We also show that the proposed algorithm can handle plane association problem incrementally in submap level, as the plane covariance can be obtained in each submap. The use of submap significantly reduces the computational cost during the optimization process, while keeping all information about planes. The proposed method is validated using both publicly available RGB-D benchmarks and datasets collected by authors. The algorithm can produce accurate trajectories and high quality 3D models on these challenging datasets, which are difficult for existing RGB-D SLAM or SFM algorithms.
Year
DOI
Venue
2019
10.1016/j.robot.2019.05.002
Robotics and Autonomous Systems
Keywords
Field
DocType
RGB-D,SLAM,3D reconstruction,Indoor mapping
Computer vision,Global structure,Global Map,Computer science,Algorithm,Artificial intelligence,RGB color model,Fuse (electrical),Covariance,3D reconstruction
Journal
Volume
ISSN
Citations 
118
0921-8890
1
PageRank 
References 
Authors
0.36
0
5
Name
Order
Citations
PageRank
Jun Wang19228736.82
Jingwei Song2111.88
Liang Zhao310013.74
Shoudong Huang475562.77
Rong Xiong55314.05