A scheme is developed for classifying the types of motion perceived by a humanlike robot. It is assumed that the robot receives visual images of the scene using a perspective system model. Equations, theorems, concepts, clues, etc., relating the objects, their positions, and their motion to their images on the focal plane are presented. >

http://ieeexplore.ieee.org/iel2/815/3148/00100651.pdf

Robot vision

During last decade the scientific research on Unmanned Aerial Vehicless (UAVs) increased spectacularly and led to the design of multiple types of aerial platforms. The major challenge today is the development of autonomously operating aerial agents capable of completing missions independently of human interaction. To this extent, visual sensing techniques have been integrated in the control pipeline of the UAVs in order to enhance their navigation and guidance skills. The aim of this article is to present a comprehensive literature review on vision based applications for UAVs focusing mainly on current developments and trends. These applications are sorted in different categories according to the research topics among various research groups. More specifically vision based position-attitude control, pose estimation and mapping, obstacle detection as well as target tracking are the identified components towards autonomous agents. Aerial platforms could reach greater level of autonomy by integrating all these technologies onboard. Additionally, throughout this article the concept of fusion multiple sensors is highlighted, while an overview on the challenges addressed and future trends in autonomous agent development will be also provided.

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Survey on Computer Vision for UAVs: Current Developments and Trends

AIAA Guidance, Navigation, and Control Conference

This paper presents a complete review of computer vision algorithms and vision-based intelligent applications, that are developed in the field of the Unmanned Aerial Vehicles (UAVs) in the latest decade. During this time, the evolution of relevant technologies for UAVs; such as component miniaturization, the increase of computational capabilities, and the evolution of computer vision techniques have allowed an important advance in the development of UAVs technologies and applications. Particularly, computer vision technologies integrated in UAVs allow to develop cutting-edge technologies to cope with aerial perception difficulties; such as visual navigation algorithms, obstacle detection and avoidance and aerial decision-making. All these expert technologies have developed a wide spectrum of application for UAVs, beyond the classic military and defense purposes. Unmanned Aerial Vehicles and Computer Vision are common topics in expert systems, so thanks to the recent advances in perception technologies, modern intelligent applications are developed to enhance autonomous UAV positioning, or automatic algorithms to avoid aerial collisions, among others. Then, the presented survey is based on artificial perception applications that represent important advances in the latest years in the expert system field related to the Unmanned Aerial Vehicles. In this paper, the most significant advances in this field are presented, able to solve fundamental technical limitations; such as visual odometry, obstacle detection, mapping and localization, et cetera. Besides, they have been analyzed based on their capabilities and potential utility. Moreover, the applications and UAVs are divided and categorized according to different criteria.

Survey of computer vision algorithms and applications for unmanned aerial vehicles

This paper presents a methodology for coordinated standoff tracking of moving target groups using multiple unmanned aerial vehicles (UAVs). The vector field guidance approach for a single UAV is first applied to track a group of targets by defining a variable standoff orbit to be followed, which can keep all targets within the field-of-view of the UAV. A new feedforward term is included in the guidance command considering variable standoff distance, and the convergence of the vector field to the standoff orbit is analyzed and enhanced by adjusting radial velocity using two active measures associated with vector field generation. Moreover, for multiple group tracking by multiple UAVs, a two-phase approach is proposed as a suboptimal solution for a Non-deterministic Polynomial-time hard (NP-hard) problem, consisting of target clustering/assignment and cooperative standoff group tracking with online local replanning. Lastly, localization sensitivity to the group of targets is investigated for different angular separations between UAVs and sensing configurations. Numerical simulations are performed using randomly moving ground vehicles with multiple UAVs to verify the feasibility and benefit of the proposed approach.

Coordinated standoff tracking of moving target groups using multiple UAVs

This paper presents strategies for standoff target tracking by a team of unmanned aircrafts using vector field. Many methods to the vector field approach were investigated in other papers, but a modified vector field is introduced to obtain new interesting characteristics in this paper. The modified vector field satisfies more constraints. We introduce two guidance modes to track a target: one is a capturing mode and the other one is a loitering mode. In the capturing mode, aircrafts can arrive at desired positions and time, i.e., unmanned aircrafts can capture a target simultaneously from all sides. After a target is captured, the guidance mode is changed to the loitering mode. Then the relative spacing among aircrafts is controlled by a standoff distance command and a speed command. Hence, they track a target with a desired formation.

Standoff Target Tracking using a Vector Field for Multiple Unmanned Aircrafts

This paper focuses on unmanned aircraft guidance laws for a straight path and a circular orbit following using the vector field approach. The vector fields introduced in this paper can be applied to not only path following, but also other purposes such as arrival position, angle, and time control. Therefore, they could be applied to various missions providing advantages over other previous vector fields. Stability and performance of the path following has been proved and analyzed using the classical control theory. Simulation using a six degrees-of-freedom aircraft model shows that these guidance laws are effective for the missions even under the existence of wind disturbance. Furthermore, flight experiments with a blended-wing-body type unmanned aircraft are performed to evaluate the proposed vector field guidance algorithm.

Unmanned Aircraft Vector Field Path Following with Arrival Angle Control

This paper proposes a novel algorithm for tracking a target from UAV(Unmanned Aerial Vehicle). No knowledge is assumed about the target, apart from being in motion. The algorithm includes optical flow to compute velocity field of sequential frames. Velocity field varies radically near the boundary of the target. This simple fact enables to segregate the target and background from the scene and to obtain their velocities. With the aid of simple geolocation method, we can get the absolute location and velocity of the target and this will enable target tracking for UAV based on optical flow.

Tracking an unknown moving target from UAV: Extracting and localizing an moving target with vision sensor based on optical flow

A vision-based terrain referenced navigation (TRN) system is addressed for autonomous navigation of unmanned aerial vehicles (UAVs). A typical TRN algorithm blends inertial navigation data with measured terrain information to estimate vehicle's position. In this paper, however, we replace the low-cost inertial navigation system (INS) with a monocular vision system. The homography decomposition algorithm is utilized to estimates the relative translational motion using features on the ground with simple assumptions. A numerical integration point-mass filter based on Bayesian estimation is employed to combine the translation information obtained from the vision system with the measured terrain height. Numerical simulations are constructed to evaluate the performance of the proposed method. The results show that the precise autonomous navigation of unmanned aircrafts is achieved by the vision-based TRN algorithm.

Vision-based Terrain Referenced Navigation for Unmanned Aerial Vehicles using Homography Relationship

The robust marker tracking and relative navigation algorithms are presented for precise UAV vision-based autonomous landing. To recognize the marker in close-range, the concentric circles are adopted as the marker with ellipse fitting algorithm based on Direct Least Square. We assume that IMU provides vehicle's attitude and altitude so that we consider GPS-denied situation. Also multiple ellipses are used to estimate its center pixel coordinate makes UAV land more accurately. To verify the vision-based relative navigation algorithm we suggest, numerical simulations are obtained by using virtual reality toolbox in MATLAB™. We predetermine the true position and attitude of UAV, and the result of the relative position calculated from vision software including the filter is compared. The simulation results show that the algorithm is robust and very accurate.

Dongjin Lee

Papers

Standoff Target Tracking using a Vector Field for Multiple Unmanned Aircrafts

Unmanned Aircraft Vector Field Path Following with Arrival Angle Control

Tracking an unknown moving target from UAV: Extracting and localizing an moving target with vision sensor based on optical flow

Vision-based Terrain Referenced Navigation for Unmanned Aerial Vehicles using Homography Relationship

Robust marker tracking algorithm for precise UAV vision-based autonomous landing