Indoor and outdoor in-flight odometry based solely on optic flows with oscillatory trajectories

Abstract

Estimating distance traveled is a frequently arising problem in robotic applications designed for use in environments where GPS is only intermittently or not at all available. In UAVs, the presence of weight and computational power constraints makes it necessary to develop odometric strategies based on minimilastic equipment. In this study, a hexarotor was made to perform up-and-down oscillatory movements while flying forward in order to test a self-scaled optic flow based odometer. The resulting self-oscillatory trajectory generated series of contractions and expansions in the optic flow vector field, from which the flight height of the hexarotor could be estimated using an Extended Kalman Filter. For the odometry, the downward translational optic flow was scaled by this current visually estimated flight height before being mathematically integrated to obtain the distance traveled. Here we present three strategies based on sensor fusion requiring no, precise or rough prior knowledge of the optic flow variations generated by the sinusoidal trajectory. The “rough prior knowledge” strategy is based on the shape and timing of the variations in the optic flow. Tests were performed first in a flight arena, where the hexarotor followed a circular trajectory while oscillating up and down over a distance of about [Formula: see text] m under illuminances of [Formula: see text] lux and [Formula: see text] lux. Preliminary field tests were then performed, in which the hexarotor followed a longitudinal bouncing [Formula: see text]-long trajectory over an irregular pattern of grass.

Safety Net Detection by Optic Flow Processing

“…Université, CNRS, ISM, 13009 Marseille, France {xavier.daini, charles.coquet, franck.ruffier}@univ-amu.fr 2 Université de Bourgogne, LIB EA 7534, 21000 Dijon, France romain.raffin@u-bourgogne.fr 3 Université de Lorraine, Arts et Métiers Institute of Technology, LCFC, HESAM Université, F-57070 Metz, France thibaut.raharijaona@univ-lorraine.fr a drone without accelerometer [11], [12] and to perform minimalistic visual odometry [13].…”

Real-time braking control based on optic flow divergence onboard an underwater vehicle

“…Translational optic flow has been employed for controlled landing [22], uneven terrain following [9] and localization [14,15]. Optic flow divergence has been used for visually controlled landing [11,24,8,12], honeybee-inspired visual odometry [1] and obstacle avoidance [21,26]. Methods for measuring optic flow divergence were tested onboard UAVs with a monocular camera for landing [8,12] and optic flow sensors ORCID(s): to perform odometry [1].…”

“…Optic flow divergence has been used for visually controlled landing [11,24,8,12], honeybee-inspired visual odometry [1] and obstacle avoidance [21,26]. Methods for measuring optic flow divergence were tested onboard UAVs with a monocular camera for landing [8,12] and optic flow sensors ORCID(s): to perform odometry [1]. The optic flow divergence was also used to estimate the relative distance between a UAV and the ground using an Extended Kalman Filter (EKF) [1].…”

“…Methods for measuring optic flow divergence were tested onboard UAVs with a monocular camera for landing [8,12] and optic flow sensors ORCID(s): to perform odometry [1]. The optic flow divergence was also used to estimate the relative distance between a UAV and the ground using an Extended Kalman Filter (EKF) [1]. While optic flow has been extensively studied for aerial robotics, very few studies have explored the possibility of using optic flow cues for UUV navigation as well.…”

Synchronization of a New Light-Flashing Shield With an External-Triggered Camera

“…autonomous navigation, surveillance, etc) has been significantly enhanced [1]- [4], yet environments with poor light conditions stand as a major challenge since such conditions worsen the sensors performance and reliability. Developing new sensors technologies, such as bio-inspired retinas [5], [6], event-based cameras [7], [8], photon avalanche diode arrays [9], and the heterojunction of different semiconductors [10], is a solution adopted to enhance VBTs in the absence of proper light conditions [11]- [13]. On the other hand, due to their implementation easiness and relative low application costs, image processing techniques, as iterative statistic 3D-points alignment [14], artificial neural networks [15], and different LIDAR data treatments [16], have been a more preferred option that had equally demonstrated to improve VBTs in obscured scenarios.…”