Monocular Vision-based Aircraft Ground Obstacle Classification

Abstract

This article presents the first steps towards extending the applicability of the author's monocular vision-based aircraft sense and avoid method for steady ground obstacles. The goal is to decide if a ground obstacle is a collision threat or not. The focus of the development is real-time onboard applicability that's why simple calculations are proposed. After extending the calculation formulae for steady obstacles the results of a software-in-the-loop simulation campaign are presented for car and tower obstacles. The results are all acceptable so further developments will target a proper avoidance strategy and real flight tests.

Applicability of Different Mono Camera-based Aircraft Sense and Avoid Methods for Steady and Linear Path Intruders

“…TTCPA denotes the Time to closest point of approach t CP A required to reach CPA from the current position. In case of steady intruder β CP A = ±90 • as the smallest distance from own trajectory is the perpendicular one (see [18]). Though rectangular shape models were explored for steady obstacles in [19] and [20] here only the disc projection model in Fig.…”

“…The developed solution estimates the so-called closest point of approach (CPA) relative to the unknown intruder size and the time to reach it (t CP A ). The ground obstacle related works considered free own trajectories but steady obstacles [18], [19] and [20] finally providing a method to estimate the obstacle's absolute position (relative to the own position upon observation of the obstacle) and size.…”

Comparison of Mono Camera-based Static Obstacle Position Estimation Methods for Automotive Application

“…D. Method based-on TTC and CPA estimation (SAA) This method was developed by the author for aircraft sense and avoid application as published in [12] and [13]. The basic formulas relate forward (Z) and side distances (X) and the object size (W) to the measurable image parameters (x,S):…”

“…The author targets to compare existing mono camera-based methods without radar assistance as this is an extensively researched field in automotive ( [2], [3], [4], [5], [6], [7], [8], [9], [10], [11]) and a good opportunity to evaluate his mono camera-based obstacle avoidance algorithm ( [12], [13]) used in aerospace until now. The final goal is to select the best algorithm which can be further evaluated and possibly tested in automotive application.…”

“…The fourth method is the author's aerospace method [12] which considers scale change with constant velocity [13] and estimates TTC and the closest point of approach (CPA) which is the ratio of side distance and obstacle width. It is denoted by SAA (originally aircraft sense and avoid).…”