High speed obstacle avoidance using monocular vision and reinforcement learning
ICML '05 Proceedings of the 22nd international conference on Machine learning
Cover trees for nearest neighbor
ICML '06 Proceedings of the 23rd international conference on Machine learning
FAB-MAP: Probabilistic Localization and Mapping in the Space of Appearance
International Journal of Robotics Research
Learning for control from multiple demonstrations
Proceedings of the 25th international conference on Machine learning
80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
From the Test Benches to the First Prototype of the muFly Micro Helicopter
Journal of Intelligent and Robotic Systems
Estimating 3D Positions and Velocities of Projectiles from Monocular Views
IEEE Transactions on Pattern Analysis and Machine Intelligence
An improved algorithm finding nearest neighbor using Kd-trees
LATIN'08 Proceedings of the 8th Latin American conference on Theoretical informatics
Flapping flight for biomimetic robotic insects: part II-flight control design
IEEE Transactions on Robotics
Visual SLAM for Flying Vehicles
IEEE Transactions on Robotics
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We consider the problem of autonomously flying a helicopter in indoor environments. Navigation in indoor settings poses two major challenges. First, real-time perception and response is crucial because of the high presence of obstacles. Second, the limited free space in such a setting places severe restrictions on the size of the aerial vehicle, resulting in a frugal payload budget. We autonomously fly a miniature RC helicopter in small known environments using an on-board light-weight camera as the only sensor. We use an algorithm that combines data-driven image classification with optical flow techniques on the images captured by the camera to achieve real-time 3D localization and navigation. We perform successful autonomous test flights along trajectories in two different indoor settings. Our results demonstrate that our method is capable of autonomous flight even in narrow indoor spaces with sharp corners.