Mr. Yi Xiang Profile

Yi Xiang

at Tianjin Univ

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 5 November 2018 Paper

Bag-of-words based loop-closure detection in visual SLAM

Zhang Jiachen, Dahang Ai, Yi Xiang, Xin Chang, Yi Wang, Xiaodong Chen

Proceedings Volume 10816, 1081618 (2018) https://doi.org/10.1117/12.2500819

KEYWORDS: Associative arrays, Visualization, Detection and tracking algorithms, Environmental sensing, Data processing, Error analysis, Reconstruction algorithms, Optoelectronics, Sensors, Feature extraction

Read Abstract +

The loop-closure detection module in visual SLAM eliminates cumulative error from the front-end by detecting the fact that the camera passes through the same place as it passed before, providing the back-end optimization with constraints among longer intervals apart from neighboring frames, resulting in globally consistent trajectories and maps. In order to solve the problem of time-consuming and poor robustness existed in feature point matching based loop-closure detection algorithm, the article uses the Bag-of-words model based on ORB features to find loop-closure and performs spatial consistency verification on results. Firstly, the ORB feature points of image data sets are clustered to construct a dictionary. In order to complete query operations within logarithmic time, the data structure of k-ary tree is adopted. K-means++ clustering is implemented layer by layer to ensure uniform clustering. Afterwards with words bag, an image is expressed by a vector. The article uses Term Frequency-Inverse Document Frequency to assess the importance of each word so as to obtain an more effective description. Finally, the article select key frames to implement the detection. In this way repetitive detection of similar loops can be avoided and coverage of the entire environment can be guaranteed .The L1 norm is used to calculate the similarity scores between key frames. The scores are normalized using a priori similarity thus avoiding the introduction of absolute similarity thresholds, which makes the algorithm more adaptable. To remove wrong loop-closure, the spatial consistency test based on Pose Graph optimization is performed on detection results. Experiments show that the loop-closure detection algorithm used in the article has a good recall rate when the accuracy rate is high.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years