ICUAS 2021 Paper Abstract

Close

Paper FrC1.2

Bergantin, Lucia (Aix-Marseille Université), Raharijaona, Thibaut (Université de Lorraine), Ruffier, Franck (Aix-Marseille Université)

Estimation of the Distance from a Surface Based on Local Optic Flow Divergence

Scheduled for presentation during the Regular Session "Estimation and Bio-inspired" (FrC1), Friday, June 18, 2021, 14:20−14:40, Macedonia Hall

2021 International Conference on Unmanned Aircraft Systems (ICUAS), June 15-18, 2021, Athens, Greece

This information is tentative and subject to change. Compiled on March 29, 2024

Keywords Biologically Inspired UAS, Micro- and Mini- UAS, Navigation

Abstract

Estimating the distance from a surface is a well-known problem for all kinds of applications involving robots moving in an unknown environment. For flying robots this issue is often coupled with weight constraints, from which the importance of carrying out the estimation of distances with minimalistic equipment. In this study, we present a method to exploit the optic flow divergence cue in order to assess the distance from a surface by means of an Extended Kalman Filter. First, we demonstrated mathematically that the optic flow divergence can be assessed by computing the subtraction between two local optic flow magnitudes. Then, we tested this method on a test bench consisting of two on-the-shelf optic flow sensors performing a back-and-forth oscillatory movement in front of a static or moving panorama. Our findings showed that the optic flow divergence measured as a subtraction of two local optic flow magnitudes was in line with the optic flow divergence computed theoretically under two different lighting conditions. Thus, we were able to use the optic flow divergence measured to assess the distance from the panorama, both when static and when in movement, for low (120 lux) and bright (974 lux) illuminance respectively. Future work will focus on the implementation of this method on a micro-flier to estimate the height of flight from a surface, with little mass and computational power.

 

 

All Content © PaperCept, Inc.

This site is protected by copyright and trademark laws under US and International law.
All rights reserved. © 2002-2024 PaperCept, Inc.
Page generated 2024-03-29  04:35:12 PST  Terms of use