Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Camera phone based motion sensing: interaction techniques, applications and performance study
UIST '06 Proceedings of the 19th annual ACM symposium on User interface software and technology
Real-time implementation of airborne inertial-SLAM
Robotics and Autonomous Systems
Two years of Visual Odometry on the Mars Exploration Rovers: Field Reports
Journal of Field Robotics - Special Issue on Space Robotics
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
Outdoors augmented reality on mobile phone using loxel-based visual feature organization
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Pose tracking from natural features on mobile phones
ISMAR '08 Proceedings of the 7th IEEE/ACM International Symposium on Mixed and Augmented Reality
Comparing image-based localization methods
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Monocular omnidirectional visual odometry for outdoor ground vehicles
ICVS'08 Proceedings of the 6th international conference on Computer vision systems
Appearance-Guided Monocular Omnidirectional Visual Odometry for Outdoor Ground Vehicles
IEEE Transactions on Robotics
Quadrocopter control using an on-board video system with off-board processing
Robotics and Autonomous Systems
Image-Based Attitude Control of a Remote Sensing Satellite
Journal of Intelligent and Robotic Systems
Journal of Intelligent and Robotic Systems
Autonomous Flight using a Smartphone as On-Board Processing Unit in GPS-Denied Environments
Proceedings of International Conference on Advances in Mobile Computing & Multimedia
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An unmanned aerial vehicle (UAV) needs to orient itself in its operating environment to fly autonomously. Localisation methods based on visual data are independent of erroneous GPS measurements or imprecise inertial sensors. In our approach, a quadrocopter first establishes an image database of the environment. Afterwards, the quadrocopter is able to locate itself by comparing a current image taken of the environment with earlier images in the database. Therefore, characteristic image features are extracted which can be compared efficiently. We analyse three feature extraction methods and five feature similarity measures. The evaluation is based on two datasets recorded under real conditions. The computations are performed on a Nokia N95 mobile phone, which is mounted on the quadrocopter. This lightweight, yet powerful device offers an integrated camera and serves as central processing unit. The mobile phone proved to be a good choice for visual localisation on a quadrocopter.