Implementation of wide-field integration of optic flow for autonomous quadrotor navigation

By J. Conroy, Gregory M. Gremillion, B. Ranganathan, J. Humbert. 2009

200 citations recorded by the source corpus.

How researchers see this paper

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Learning monocular reactive UAV control in cluttered natural environments (2012, background): “Beyeler et al. [19] as well as Conroy et al. [20] implemented systems which exploit this fact and lead to good obstacle avoidance results.”
Ultra low-power visual odometry for nano-scale unmanned aerial vehicles (2017, background): “At the micro-scale there are several works able to map high level navigation skills in “relatively” low-power architectures, but they are still an order of magnitude away from the nanoscale power-budget [6] [7].”
A Machine Learning Approach to Visual Perception of Forest Trails for Mobile Robots (2016, background): “When the goal is obstacle avoidance, several works [21]–[23] obtained good results with simple biologically-inspired controllers based on optical flow features.”
Stereo vision-based obstacle avoidance for micro air vehicles using disparity space (2014, methodology): “Optical flow can cover a wide field of view and has been used for reactive obstacle avoidance [4], [5]; however, this only provides information when the aircraft is moving, and poor estimation of time to collision near the focus of expansion limits the ability to perceive obstacles dead ahead.”