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

Sensors

Hyungjin Kim²  Donghwa Lee²  Taekjun Oh²  Hyun-Taek Choi¹  Hyun Myung² 

[1] 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; E-Mail:;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; E-Mails:

关键词: localization;    monocular camera;    probabilistic feature map;    3D-to-2D matching correspondences;    image data set;   

DOI  :  10.3390/s150921636

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.