ORB-SHOT SLAM: Trajectory Correction by 3D Loop Closing Based on Bag-of-Visual-Words (BoVW) Model for RGB-D Visual SLAM
| Autor: | Zheng Chai, Takafumi Matsumaru |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
0209 industrial biotechnology
General Computer Science GeneralLiterature_INTRODUCTORYANDSURVEY Computer science business.industry Shot (filmmaking) ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 02 engineering and technology Loop closing 020901 industrial engineering & automation Bag-of-words model in computer vision Computer graphics (images) 0202 electrical engineering electronic engineering information engineering Trajectory 020201 artificial intelligence & image processing Computer vision Artificial intelligence Electrical and Electronic Engineering business Orb (optics) |
| Zdroj: | Journal of Robotics and Mechatronics. 29:365-380 |
| ISSN: | 1883-8049 0915-3942 |
| DOI: | 10.20965/jrm.2017.p0365 |
| Popis: | [abstFig src='/00290002/10.jpg' width='300' text='Visual odometry + trajectory correction' ] This paper proposes the ORB-SHOT SLAM or OS-SLAM, which is a novel method of 3D loop closing for trajectory correction of RGB-D visual SLAM. We obtain point clouds from RGB-D sensors such as Kinect or Xtion, and we use 3D SHOT descriptors to describe the ORB corners. Then, we train an offline 3D vocabulary that contains more than 600,000 words by using two million 3D descriptors based on a large number of images from a public dataset provided by TUM. We convert new images to bag-of-visual-words (BoVW) vectors and push these vectors into an incremental database. We query the database for new images to detect the corresponding 3D loop candidates, and compute similarity scores between the new image and each corresponding 3D loop candidate. After detecting 2D loop closures using ORB-SLAM2 system, we accept those loop closures that are also included in the 3D loop candidates, and we assign them corresponding weights according to the scores stored previously. In the final graph-based optimization, we create edges with different weights for loop closures and correct the trajectory by solving a nonlinear least-squares optimization problem. We compare our results with several state-of-the-art systems such as ORB-SLAM2 and RGB-D SLAM by using the TUM public RGB-D dataset. We find that accurate loop closures and suitable weights reduce the error on trajectory estimation more effectively than other systems. The performance of ORB-SHOT SLAM is demonstrated by 3D reconstruction application. |
| Databáze: | OpenAIRE |
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