ABSTRACT We present a technique to parameterize skin deformation by skeletal motion and to transfer the deformation style from one character to another. We decompose skin deformation into time‐varying signals and basis matrices by using dimension reduction techniques and then approximate the time‐varying signals by using radial basis functions with respect to joint angles that define skeletal motion. This decomposition reduces the size of deformation data to a small number of time‐varying signals that represent the complex role of muscle action. The subsequent parameterization yields a fast and intuitive control of characters; thus, it allows us to construct faithful skin deformations quickly as skeletal bones move. The representation of our parameterization allows us to capture and transfer a derived deformation style to another skeleton–skin structure without considering the input dimension of the deformation data. This style transfer can be used as a basis for realistically animating variants of sample characters that have the same skeletal topology. Parameterization of skin deformation and its style transfer can be performed within a small amount of error once the preprocessing time and control of the deformation is carried out in real time by our graphics processing unit implementation. Copyright © 2011 John Wiley & Sons, Ltd.

This paper presents a robust multi-loop airborne SLAM structure which also augments wind information. The air velocity observation from an air data system can be used to estimate the error of the on-board Inertial Navigation System (INS). However, due to a priori unknown wind velocity, it cannot directly be used for this purpose. This can be tackled by augmenting the unknown wind velocity into the state vector of SLAM, simultaneously estimating INS, map and wind. This paper proposes a multi-loop SLAM architecture, where the periodic velocity-level SLAM loop limits the INS errors of the velocity effectively, and the aperiodic position-level SLAM loop bounds the overall position error growth. This can significantly increase the consistency of airborne SLAM at the time of loop closure. Simulation results show that the unknown wind vector can be estimated consistently and the robustness of airborne SLAM improves significantly

In this paper, we present a model-driven approach to RTOS-based embedded software development and an automated tool that can produce RTOS-specific code. Our contributions are two fold. First, we defined generic RTOS APIs (application programming interfaces) that can capture most of typical RTOS services but are not bound to any specific RTOS. The generic RTOS APIs can be used as a means for describing application's RTOS-related behavior at an early design stage. We then developed an automated transformation tool that can generate functional code by transforming generic RTOS APIs into RTOS-specific APIs. Our tool, called TransPI, is able to produce 'C' code aimed at POSIX-compliant RTOSs, and is also configurable to target other RTOSs that do not conform to the POSIX standard

This paper addresses the computational complexity associated with 6-DoF inertial SLAM by recasting the filter into an indirect or complimentary structure. By doing so the standard non-linear inertial SLAM algorithm is piecewise linearised, providing two significant breakthroughs. Firstly, through the process of linearisation, the filter estimates the errors in the inertial states (errors in position, velocity and attitude) as opposed to the inertial states themselves, thus significantly reducing the amount of prediction updates required and hence the computational burden generally associated with inertial SLAM. Secondly, due to the piecewise linear nature of the underlying model, a new level of understanding of the observability of SLAM can be carried out through the use of the stripped observability matrix. This paper focuses on the 6-DoF implementation of SLAM in an airborne vehicle. Results illustrating that the new filter structure can efficiently estimate the errors and provide a navigation solution which is comparable to the standard implementation with significantly less computational cost is provided, along with theoretical results on the observability of 6-DoF SLAM.

This paper presents results of the application of simultaneous localisation and map building (SLAM) for an uninhabited aerial vehicle (UAV). Single vision camera and inertial measurement unit (IMU) are installed in a UAV platform. The data taken from a flight test is used to run the SLAM algorithm. Results show that both the map and the vehicle uncertainty are corrected even though the model of the system and observation are highly non-linear. The results, however, also indicate that further work of observability and the relationship between vehicle model drift and the number and the location of landmarks need to be further analysed given the highly dynamic nature of the system.