Fast normalized cross-correlation for measuring distance to objects using optic flow, applied for helicopter obstacle detection

Abstract

The fast normalized cross-correlation (NCC) calculation method presented here features low computational requirements, which makes it suitable for being implemented in real time onboard micro-controllers with very few computational resources. This method was adapted for making distance measurements, using a high speed optic flow sensor operating at 20m/s. An application of this study is to develop a proof of concept of an innovative optic flow sensor fixed at the tip of an helicopter's blade (from Airbus Helicopter) to measure the distance to various obstacles (wall, cliff...) in the azimuthal plane (rotor plane) during hovering flight. Due to its small size, this sensor developed in this paper can also be also adapted for Micro aerial vehicles (MAVs). This method of calculation requires less memory than the reference method, at the expense of some extra arithmetical operations, but it is still significantly lighter than the classical NCC method. An algorithm for implementing this method in real-time robotic applications is presented. Experimental results confirm the efficiency of this highly time-saving method.

Applying Regressive Machine Learning Techniques in Determination of COVID-19 Vaccinated Patients’ Influence on the Number of Confirmed and Deceased Patients

“…The "full" cross-correlation is used, in which zero padding is applied to elements to calculate the cross-correlation values for each overlap of the two time-series. To obtain the correlation coefficient, normalized cross-correlation with time shift can be used, defined as the discrete normalized temporal cross-correlation, per [30]:…”