Terrain Modelling from Feature Primitives

We introduce a compact hierarchical procedural model that combines feature‐based primitives to describe complex terrains with varying level of detail. Our model is inspired by skeletal implicit surfaces and defines the terrain elevation function by using a construction tree. Leaves represent terrain features and they are generic parametrized skeletal primitives, such as mountains, ridges, valleys, rivers, lakes or roads. Inner nodes combine the leaves and subtrees by carving, blending or warping operators. The elevation of the terrain at a given point is evaluated by traversing the tree and by combining the contributions of the primitives. The definition of the tree leaves and operators guarantees that the resulting elevation function is Lipschitz, which speeds up the sphere tracing used to render the terrain. Our model is compact and allows for the creation of large terrains with a high level o detail using a reduced set of primitives. We show the creation of different kinds of landscapes and demonstrate that our model allows to efficiently control the shape and distribution of landform features.     

Air‐ground Matching: Appearance‐based GPS‐denied Urban Localization of Micro Aerial Vehicles

In this paper, we address the problem of globally localizing and tracking the pose of a camera‐equipped micro aerial vehicle (MAV) flying in urban streets at low altitudes without GPS. An image‐based global positioning system is introduced to localize the MAV with respect to the surrounding buildings. We propose a novel air‐ground image‐matching algorithm to search the airborne image of the MAV within a ground‐level, geotagged image database. Based on the detected matching image features, we infer the global position of the MAV by back‐projecting the corresponding image points onto a cadastral three‐dimensional city model. Furthermore, we describe an algorithm to track the position of the flying vehicle over several frames and to correct the accumulated drift of the visual odometry whenever a good match is detected between the airborne and the ground‐level images. The proposed approach is tested on a 2 km trajectory with a small quadrocopter flying in the streets of Zurich. Our vision‐based global localization can robustly handle extreme changes in viewpoint, illumination, perceptual aliasing, and over‐season variations, thus outperforming conventional visual place‐recognition approaches. The dataset is made publicly available to the research community. To the best of our knowledge, this is the first work that studies and demonstrates global localization and position tracking of a drone in urban streets with a single onboard camera.     

Factor Graph Modeling of Rigid‐body Dynamics for Localization,Mapping, and Parameter Estimation of a Spinning Object in Space

This paper presents a new approach for solving the simultaneous localization and mapping problem for inspecting an unknown and uncooperative object that is spinning about an arbitrary axis in space. This approach probabilistically models the six degree‐of‐freedom rigid‐body dynamics in a factor graph formulation. Using the incremental smoothing and mapping system, this method estimates a feature‐based map of the target object, as well as this object's position, orientation, linear velocity, angular velocity, center of mass, principal axes, and ratios of inertia. This solves an important problem for spacecraft proximity operations. Additionally, it provides a generic framework for incorporating rigid‐body dynamics that may be applied to a number of other terrestrial‐based applications. To evaluate this approach, the Synchronized Position Hold Engage Reorient Experimental Satellites (SPHERES) were used as a testbed within the microgravity environment of the International Space Station. The SPHERES satellites, using body‐mounted stereo cameras, captured a dataset of a target object that was spinning at ten rotations per minute about its unstable, intermediate axis. This dataset was used to experimentally evaluate the approach described in this paper, and it showed that it was able to estimate a geometric map and the position, orientation, linear and angular velocities, center of mass, and ratios of inertia of the target object.     

Short‐Time Linear Quadratic Form Technique for Estimating Fast‐Varying Parameters in Feedback Loops

The precision of a closed‐loop controller system designed for an uncertain plant depends strongly upon the maximum extent to which it is possible to track the trend of time‐varying parameters of the plant. The aim of this study is to describe a new parameter estimation algorithm that is able to follow fast‐varying parameters in closed‐loop systems. The short‐time linear quadratic form (STLQF) estimation algorithm introduced in this paper is a technique for tracking time‐varying parameters based on short‐time analysis of the regressing variables in order to minimize locally a linear quadratic form cost function. The established cost function produces a linear combination of errors with several delays. To meet this objective, mathematical development of the STLQF estimation algorithm is described. To implement the STLQF algorithm, the algorithm is applied to a planar mobile robot with fast‐varying parameters of inertia and viscous and coulomb frictions. Next, performance of the proposed algorithm is assessed against noise effects and variation in the type of parameters.     

Simultaneous Perturbation Stochastic Approximation with Norm‐Limited Update Vector

This paper addresses the convergence of simultaneous perturbation stochastic approximation (SPSA) with a norm‐limited update vector. We first illustrate an unstable solution of the standard SPSA algorithm which motivates the consideration of a modified version, where the norm of the update vector is limited to a certain value. Next, a result on the almost‐sure convergence is presented by reducing the modified algorithm into the standard SPSA algorithm and restricting the probability distribution for the perturbation to a Bernoulli distribution. Finally, we apply the modified algorithm to a system identification problem to demonstrate its performance.     

Nonlinear Gains Recursive Sliding Mode Dynamic Surface Control with Integral Action

In this paper, by introducing a function with nonlinear gains and developing sliding surfaces with integral action in the dynamic surface control (DSC) recursive procedure, a novel DSC strategy is proposed. The drawbacks of conventional DSC methods, such as being sensitive to the design constant of the first order low‐pass filter and being unable to achieve zero steady‐state error for step reference signal, are overcome. Moreover, improvement of transient performance and reduction of control effort can be obtained. The stability analysis shows that the proposed new approach can guarantee semi‐global uniform ultimate boundedness (SGUUB) of all closed‐loop signals and that the ultimate tracking error bound in regulation can be made arbitrarily small.     

Heuristic Dynamic Programming Using Echo State Network For Multivariable Tracking Control Of Wastewater Treatment Process

This paper proposes a heuristic dynamic programming (HDP) scheme to simultaneously control the dissolved oxygen concentration and the nitrate level in wastewater treatment processes (WWTP). Unlike traditional HDP schemes, the optimal control values are calculated in an analytical way by the proposed HDP controller. It can reduce the learning burden of the HDP controller to a great extent. The system model and the evaluation index J are approximated by two echo state networks (ESNs). Gradient‐based learning algorithms are employed to train both ESNs online, and the convergence of the training algorithm is investigated based on Lyapunov theory. The performance of the proposed ESN‐based HDP (E‐HDP) controller is tested and evaluated on a WWTP benchmark. Experimental results demonstrate that the proposed approach can achieve effective performance.     

Key-dependent 3D model hashing for authentication using heat kernel signature

Multimedia-based hashing is considered an important technique for achieving authentication and copy detection in digital contents. However, 3D model hashing has not been as widely used as image or video hashing. In this study, we develop a robust 3D mesh-model hashing scheme based on a heat kernel signature (HKS) that can describe a multi-scale shape curve and is robust against isometric modifications. We further discuss the robustness, uniqueness, security, and spaciousness of the method for 3D model hashing. In the proposed hashing scheme, we calculate the local and global HKS coefficients of vertices through time scales and 2D cell coefficients by clustering HKS coefficients with variable bin sizes based on an estimated L² risk function, and generate the binary hash through binarization of the intermediate hash values by combining the cell values and the random values. In addition, we use two parameters, bin center points and cell amplitudes, which are obtained through an iterative refinement process, to improve the robustness, uniqueness, security, and spaciousness further, and combine them in a hash with a key. By evaluating the robustness, uniqueness, and spaciousness experimentally, and through a security analysis based on the differential entropy, we verify that our hashing scheme outperforms conventional hashing schemes.     

Flocking under hierarchical leadership with a free‐will leader

Recently, a model for flocking was introduced by Cucker and Smale together with a proof of convergence. This proof established unconditional convergence to flocking (i.e., to a common velocity), provided the interaction between agents was strong enough and conditional convergence otherwise. The strength of the interaction is measured by a parameter β, and the critical value at which unconditional convergence stops holding is β = 1 ∕ 2. This model was extended by Shen to allow for a hierarchical leadership structure among the agents, and similar convergence results were proved. But, for discrete time, convergence result was only for the flock with an overall leader moving with a constant velocity. In this note, we establish convergence result for the flock with a free‐will leader. Copyright © 2012 John Wiley & Sons, Ltd.