<title>Three-dimensional computer vision for micro air vehicles</title>

Abstract

Results are described of an ongoing project whose goal is to provide advanced Computer Vision for small low flying autonomous aircraft. The work consists of two parts; range-based vision for object recognition and pose estimation, and monocular vision for navigation and collision avoidance. A wide variety of range imaging methods were considered for the former, and it was found that a promising approach is multi-ocular stereo with a pseudorandom texture projected with a xenon flash. This provides high range resolution despite motion, and can be small and light. The resulting range images, taken at a few meters range, would support the use of Tripod Operators, an efficient and general method for recognizing and localizing surface shapes in 6 DOF. This would provide the ability to recognize immediately upon encounter many kinds of targets. The monocular navigation system is based on finding corresponding features in successive images, and deducing from these the relative pose of the aircraft. Two methods are under development, based on horizon registration and point correspondences, respectively. The first can serve as a preprocessor for the second. This approach aims to continuously and accurately estimate the net motion of the vehicle.

Case Study: Micro Tactical Expendable Rigid-Wing Micro Air Vehicle

“…The main efforts concentrated on optically based systems: 1) optic flow for collision avoidance [11] and 2) monocular motion stereo to determine vehicle orientation with respect to features on the ground in six degrees of freedom and to integrate the changing position to determine ground track for navigation [12]. The main efforts concentrated on optically based systems: 1) optic flow for collision avoidance [11] and 2) monocular motion stereo to determine vehicle orientation with respect to features on the ground in six degrees of freedom and to integrate the changing position to determine ground track for navigation [12].…”

A Safety Analysis Process for the Traffic Alert and Collision Avoidance System (TCAS) and See-and-Avoid Systems on Remotely Piloted Vehicles

“…Several types of safety enhancement systems have been proposed for RPVs to compensate for their lack of conventional see-and-avoid capabilities. [6][7][8][9][10][11][12][13] These can be divided into two general categories: those that protect against cooperative threats (aircraft with transponders) and those that protect against non-cooperative threats. Sensors for each type of threat are discussed in more detail in the following sections.…”

Autonomous Landing for Indoor Flying Robots Using Optic Flow

“…Processing of images captured by an aerial robot's on-board camera has been performed on outdoor aerial robots. Common methods exploit image features provided by the horizon [10] or flying field [12] which are both absent in closed quarters. The net effect is that conventional sensors and methods, although successful outdoors, have limitations indoors and thus demand alternative approaches.…”

“…For example, GPS does not provide the re- quired precision for operation in closed quarters and GPS signals are easily jammed. Also vision-based methods that reference the horizon (Pipitone et al [10]) are also inappropriate indoors. Furthermore, closed quarters often demands being small and must fly slowly and safely in order to maneuver through halls and tunnels.…”