Simultaneous Localization and Map-Building Using Active Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visual navigation using a single camera
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Real-Time Simultaneous Localisation and Mapping with a Single Camera
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Monocular Vision Based SLAM for Mobile Robots
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
MonoSLAM: Real-Time Single Camera SLAM
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Panoramic Image Stitching using Invariant Features
International Journal of Computer Vision
Vision-based global localization and mapping for mobile robots
IEEE Transactions on Robotics
Vision-based 3-D trajectory tracking for unknown environments
IEEE Transactions on Robotics
Walkie-Markie: indoor pathway mapping made easy
nsdi'13 Proceedings of the 10th USENIX conference on Networked Systems Design and Implementation
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This paper describes a landmark detection and localization using an integrated laser-camera sensor. Laser range finder can be used to detect landmarks that are direction invariant in the laser data such as protruding edges in walls, edges of tables, and chairs. When such features are unavailable, the dependant processes will fail to function. However, in many instances, larger number of landmarks can be detected using computer vision. In the proposed method, camera is used to detect landmarks while the location of the landmark is measured by the laser range finder using laser-camera calibration information. Thus, the proposed method exploits the beneficial aspects of each sensor to overcome the disadvantages of the other sensor. While highlighting the drawbacks and limitations of single sensor based methods, an experimental results and important statistics are provided for the verification of the affectiveness sensor fusion method using Extended Kalman Filter (EKF) based simultaneous localization and mapping (SLAM) as an example application.