TY - JOUR
T1 - Distance and velocity estimation using optical flow from a monocular camera
AU - Ho, Hann Woei
AU - de Croon, Guido C.H.E.
AU - Chu, Qiping
PY - 2017/9/1
Y1 - 2017/9/1
N2 - Monocular vision is increasingly used in micro air vehicles for navigation. In particular, optical flow, inspired by flying insects, is used to perceive vehicle movement with respect to the surroundings or sense changes in the environment. However, optical flow does not directly provide us the distance to an object or velocity, but the ratio of them. Thus, using optical flow in control involves nonlinearity problems which add complexity to the controller. To deal with that, we propose an algorithm that estimates distance and velocity of the vehicle based on optical flow measured from a monocular camera and the knowledge of control inputs. This algorithm applies an extended Kalman filter to state estimation and uses the estimates for landing control. We implement and test our algorithm in computer simulation and on board a Parrot AR.Drone 2.0 to demonstrate its feasibility for micro air vehicles landings. Results of the simulation and multiple flight tests show that the algorithm is able to estimate height and velocity of the micro air vehicles accurately, and achieves smooth landings with these estimates, even in windy outdoor environments.
AB - Monocular vision is increasingly used in micro air vehicles for navigation. In particular, optical flow, inspired by flying insects, is used to perceive vehicle movement with respect to the surroundings or sense changes in the environment. However, optical flow does not directly provide us the distance to an object or velocity, but the ratio of them. Thus, using optical flow in control involves nonlinearity problems which add complexity to the controller. To deal with that, we propose an algorithm that estimates distance and velocity of the vehicle based on optical flow measured from a monocular camera and the knowledge of control inputs. This algorithm applies an extended Kalman filter to state estimation and uses the estimates for landing control. We implement and test our algorithm in computer simulation and on board a Parrot AR.Drone 2.0 to demonstrate its feasibility for micro air vehicles landings. Results of the simulation and multiple flight tests show that the algorithm is able to estimate height and velocity of the micro air vehicles accurately, and achieves smooth landings with these estimates, even in windy outdoor environments.
KW - autonomous landing
KW - Distance estimation
KW - efference copy
KW - monocular vision
KW - optical flow
UR - http://www.scopus.com/inward/record.url?scp=85029006867&partnerID=8YFLogxK
UR - http://resolver.tudelft.nl/uuid:a0abc18b-3ee0-4b4e-aac9-c3907a07dd49
U2 - 10.1177/1756829317695566
DO - 10.1177/1756829317695566
M3 - Article
SN - 1756-8293
VL - 9
SP - 198
EP - 208
JO - International Journal of Micro Air Vehicles
JF - International Journal of Micro Air Vehicles
IS - 3
ER -