Next Article in Journal
Unmasking of Olive Oil Adulteration Via a Multi-Sensor Platform
Next Article in Special Issue
An Improved WiFi Indoor Positioning Algorithm by Weighted Fusion
Previous Article in Journal
Label-Free Biosensor Imaging on Photonic Crystal Surfaces
Previous Article in Special Issue
Heading Estimation for Indoor Pedestrian Navigation Using a Smartphone in the Pocket
Article Menu

Export Article

Open AccessArticle
Sensors 2015, 15(9), 21636-21659; doi:10.3390/s150921636

A Probabilistic Feature Map-Based Localization System Using a Monocular Camera

Urban Robotics Laboratory (URL), Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro (373-1 Guseong-dong), Yuseong-gu, Daejeon 305-701, Korea
Ocean System Engineering Research Division, Korea Research Institute of Ships and Ocean Engineering (KRISO), 32 1312 Beon-gil, Yuseong-daero, Yuseong-gu, Daejeon 305-343, Korea
Author to whom correspondence should be addressed.
Academic Editor: Kourosh Khoshelham
Received: 27 April 2015 / Revised: 23 August 2015 / Accepted: 24 August 2015 / Published: 31 August 2015
(This article belongs to the Special Issue Sensors for Indoor Mapping and Navigation)
View Full-Text   |   Download PDF [2341 KB, uploaded 31 August 2015]   |  


Image-based localization is one of the most widely researched localization techniques in the robotics and computer vision communities. As enormous image data sets are provided through the Internet, many studies on estimating a location with a pre-built image-based 3D map have been conducted. Most research groups use numerous image data sets that contain sufficient features. In contrast, this paper focuses on image-based localization in the case of insufficient images and features. A more accurate localization method is proposed based on a probabilistic map using 3D-to-2D matching correspondences between a map and a query image. The probabilistic feature map is generated in advance by probabilistic modeling of the sensor system as well as the uncertainties of camera poses. Using the conventional PnP algorithm, an initial camera pose is estimated on the probabilistic feature map. The proposed algorithm is optimized from the initial pose by minimizing Mahalanobis distance errors between features from the query image and the map to improve accuracy. To verify that the localization accuracy is improved, the proposed algorithm is compared with the conventional algorithm in a simulation and realenvironments View Full-Text
Keywords: localization; monocular camera; probabilistic feature map; 3D-to-2D matching correspondences; image data set localization; monocular camera; probabilistic feature map; 3D-to-2D matching correspondences; image data set

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Kim, H.; Lee, D.; Oh, T.; Choi, H.-T.; Myung, H. A Probabilistic Feature Map-Based Localization System Using a Monocular Camera. Sensors 2015, 15, 21636-21659.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top