Abstract
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.
Highlights
Image-based localization is an important issue in robotics communities as well as computer vision communities
(j = 1, · · ·, n) where qj ∈ R2 denotes the j-th feature position from a query image and Fj ∈ R3 denotes the matched probabilistic feature consisting of its position Xfj and covariance Cfj, and n is the total number of the matching correspondences
The camera pose of the query image is estimated using 3D-to-2D matching correspondences
Summary
Image-based localization is an important issue in robotics communities as well as computer vision communities. According to [14,17,18], localization methods based on a feature map solve the real-time problem while providing high performance of matching on a large scale map These studies assume that the map has high accuracy and sufficient features. A novel localization method is needed to enhance localization accuracy by considering uncertainty of the feature map for mobile robot system during the camera pose estimation. The camera-based SLAM techniques [26,27,28,29] generate a probabilistic feature map based on a sensor system modeling and estimate the robot’s pose at the same time.
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