Changhong Fu

Onboard Robust Visual Tracking for UAVs Using a Reliable Global-Local Object Model

In this paper, we present a novel onboard robust visual algorithm for long-term arbitrary 2D and 3D object tracking using a reliable global-local object model for unmanned aerial vehicle (UAV) applications, e.g., autonomous tracking and chasing a moving target. The first main approach in this novel algorithm is the use of a global matching and local tracking approach. In other words, the algorithm initially finds feature correspondences in a way that an improved binary descriptor is developed for global feature matching and an iterative Lucas–Kanade optical flow algorithm is employed for local feature tracking. The second main module is the use of an efficient local geometric filter (LGF), which handles outlier feature correspondences based on a new forward-backward pairwise dissimilarity measure, thereby maintaining pairwise geometric consistency. In the proposed LGF module, a hierarchical agglomerative clustering, i.e., bottom-up aggregation, is applied using an effective single-link method. The third proposed module is a heuristic local outlier factor (to the best of our knowledge, it is utilized for the first time to deal with outlier features in a visual tracking application), which further maximizes the representation of the target object in which we formulate outlier feature detection as a binary classification problem with the output features of the LGF module. Extensive UAV flight experiments show that the proposed visual tracker achieves real-time frame rates of more than thirty-five frames per second on an i7 processor with 640 × 512 image resolution and outperforms the most popular state-of-the-art trackers favorably in terms of robustness, efficiency and accuracy.

A comparative study on the control of quadcopter UAVs by using singleton and non-singleton fuzzy logic controllers

Fuzzy logic controllers (FLCs) have extensively been used for the autonomous control and guidance of unmanned aerial vehicles (UAVs) due to their capability of handling uncertainties and delivering adequate control without the need for a precise, mathematical system model which is often either unavailable or highly costly to develop. Despite the fact that non-singleton FLCs (NSFLCs) have shown more promising performance in several applications when compared to their singleton counterparts (SFLCs), most of UAV applications are still realized by using SFLCs. In this paper, we explore the potential of both standard and the recently introduced centroid based NSFLCs, i.e., Sta-NSFLC and Cen-NSFLC, for the control of a quadcopter UAV under various input noise conditions using different levels of fuzzifier, and a comparative study has been conducted using the three aforementioned FLCs. We present a series of simulation-based experiments, the simulation results show that the control performances of NSFLCs are better than those of SFLC, and the Cen-NSFLC outperforms the Sta-NSFLC especially under highly noisy conditions.

RRT-based 3D path planning for formation landing of quadrotor UAVs

This paper discusses the formation landing problem of quadrotor UAVs, which is considered as a UAV leader-follower problem, avoiding static obstacles. Rapidly-exploring random tree algorithm is used to generate the path for the leader UAV firstly. In particular, specifics of tree-grow including nodes selection, parent node connection, feasible and optimal path generation are explained. Given the leader UAV position, path finding for the follower UAV is conducted to avoid both static obstacles and the leader quadrotor. Based on the intensive simulations, which are conducted in ROS-Gazebo environment, the proposed framework is considered to be applicable in real-time formation landing of quadrotor UAVs.

Autonomous navigation of UAV by using real-time model-based reinforcement learning

Autonomous navigation in an unknown or uncertain environment is one of the challenging tasks for unmanned aerial vehicles (UAVs). In order to address this challenge, it is necessary to have sophisticated high level control methods that can learn and adapt themselves to changing conditions. One of the most promising frameworks for such a purpose is reinforcement learning. In this paper, a novel model-based reinforcement learning algorithm, TEXPLORE, is developed as a high level control method for autonomous navigation of UAVs. The developed approach has been extensively tested with a quadcopter UAV in ROS-Gazebo environment. The experimental results show that our method is able to learn an efficient trajectory in a few iterations and perform actions in real-time. Moreover, we show that our approach significantly outperforms Q-learning based method. To the best of our knowledge, this is the first time that TEXPLORE has been developed to achieve autonomous navigation of UAVs.

A performance evaluation of detectors and descriptors for UAV visual tracking

This paper is made up of a series of performance evaluations of computer vision algorithms, namely detectors and descriptors. The OpenCV 3.1 implementations of these algorithms were used for these evaluations. The main purpose behind these evaluations was to determine the best algorithms to use for a UAV guidance system.

Similarity-based non-singleton fuzzy logic control for improved performance in UAVs

As non-singleton fuzzy logic controllers (NSFLCs) are capable of capturing input uncertainties, they have been effectively used to control and navigate unmanned aerial vehicles (UAVs) recently. To further enhance the capability to handle the input uncertainty for the UAV applications, a novel NSFLC with the recently introduced similarity-based inference engine, i.e., Sim-NSFLC, is developed. In this paper, a comparative study in a 3D trajectory tracking application has been carried out using the aforementioned Sim-NSFLC and the NSFLCs with the standard as well as centroid composition-based inference engines, i.e., Sta-NSFLC and Cen-NSFLC. All the NSFLCs are developed within the robot operating system (ROS) using the C++ programming language. Extensive ROS Gazebo simulation-based experiments show that the Sim-NSFLCs can achieve better control performance for the UAVs in comparison with the Sta-NSFLCs and Cen-NSFLCs under different input noise levels.

Double-input interval type-2 fuzzy logic controllers: Analysis and design

A significant number of investigations of type-1 and type-2 fuzzy logic controllers have revealed their exceptional ability to capture uncertainties in complex and nonlinear systems, particularly in real-time control applications. However, regardless of being type-1 or type-2, fuzzy logic controller design is still a complicated task due to the lack of a closed form solution of the output and an interpretable relationship between the control output and fuzzy logic controller design parameters, such as center or width of the membership functions. To simplify the design procedure further, we think every attempt to obtain such interpretable relationships is worthwhile. Accordingly, this paper aims to design a double-input interval type-2 fuzzy PID controller and obtain interpretable relationships between the input and the output of the controller. Thereafter, we deploy the novel design for the control of a Y6 coaxial tricopter unmanned aerial vehicle. Simulation results, which are realised in robot operating system (ROS) using C++ and Gazebo environment, are found to tally with the theoretical analysis and claims in the paper.

Recommended keypoint-aware tracker: Adaptive real-time visual tracking using consensus feature prior ranking

This paper deals with the problem of historical feature selection for appearance model update in feature-based tracking. In particular, we convert the feature selection procedure into a ranking process where the top-N keypoint features are ranked based on the tracking histories. To the best of our knowledge, for the first time in this paper, a consensus feature prior (CFP) recommendation system is proposed that allows us to learn and update the appearance model online within a limited model size. Furthermore, the ranking scores obtained from the proposed recommendation system also provide a conviction of recovering the tracking after its failure. Extensive experiments (more than 600,000 frames) have been done by strictly following the Visual Tracking Benchmark v1.0 protocol. The results demonstrate that our method outperforms most of the state-of-art trackers both in terms of speed and accuracy.

Recoverable recommended keypoint-aware visual tracking using coupled-layer appearance modelling

Object tracking over image sequences plays an remarkably crucial role in several computer vision applications, interalia, automated video surveillance, unmanned aerial vehicles and 3D reconstruction. In this paper, a novel, accurate, robust and recoverable real-time feature-based tracking framework is presented. The appearance modelling consists of a local and global layer. We propose a recommended keypoint-aware (RKA) tracker, which is fast and accurate, for the former, while the latter employs support vector machine (SVM) to determine the object and background, so that the RKA tracker can be recovered under possible target losing circumstances. Furthermore, the RKA tracker converts the tracking problem into the ranking of samples which provides a score of tracking confidence. Therefore, the priority switching between the local layer and global layer dependent upon the score becomes valid. Extensive experiments have been done by strictly following the visual tracking benchmark v1.0 protocol. The results demonstrate that the proposed novel method outperforms the state-of-the-art trackers in terms of robustness, speed and accuracy.