Vertical Landing for Micro Air Vehicles using Event-Based Optical Flow
arXiv:1702.00061 · doi:10.1002/rob.21764
Abstract
Small flying robots can perform landing maneuvers using bio-inspired optical flow by maintaining a constant divergence. However, optical flow is typically estimated from frame sequences recorded by standard miniature cameras. This requires processing full images on-board, limiting the update rate of divergence measurements, and thus the speed of the control loop and the robot. Event-based cameras overcome these limitations by only measuring pixel-level brightness changes at microsecond temporal accuracy, hence providing an efficient mechanism for optical flow estimation. This paper presents, to the best of our knowledge, the first work integrating event-based optical flow estimation into the control loop of a flying robot. We extend an existing 'local plane fitting' algorithm to obtain an improved and more computationally efficient optical flow estimation method, valid for a wide range of optical flow velocities. This method is validated for real event sequences. In addition, a method for estimating the divergence from event-based optical flow is introduced, which accounts for the aperture problem. The developed algorithms are implemented in a constant divergence landing controller on-board of a quadrotor. Experiments show that, using event-based optical flow, accurate divergence estimates can be obtained over a wide range of speeds. This enables the quadrotor to perform very fast landing maneuvers.
29 pages, 14 figures, under peer review
Cited by in corpus (7)
- Event-based Vision: A Survey
- Unsupervised Learning of a Hierarchical Spiking Neural Network for Optical Flow Estimation: From Events to Global Motion Perception
- Free as a Bird: Event-based Dynamic Sense-and-Avoid for Ornithopter Robot Flight
- Evolved Neuromorphic Control for High Speed Divergence-based Landings of MAVs
- Evolution of Robust High Speed Optical-Flow-Based Landing for Autonomous MAVs
- FAITH: Fast iterative half-plane focus of expansion estimation using event-based optic flow
- Parallelizing Optical Flow Estimation on an Ultra-Low Power RISC-V Cluster for Nano-UAV Navigation