Plenary Speakers

    1. Prof. Robert Mahony, Australian National University, Australian
    Robert Mahony obtained a science degree majoring in applied mathematics and geology from the Australian National University in 1989. After working for a year as a geophysicist processing marine seismic data he returned to study at ANU and obtained a PhD in systems engineering in 1994. Between 1994 and 1997 he worked as a Research Fellow in the Cooperative Research Centre for Robust and Adaptive Systems based in the Research School of Information Sciences and Engineering in ANU. From 1997 to 1999 he held a post as a post-doctoral fellow in the CNRS laboratory for Heuristics Diagnostics and complex systems (Heudiasyc), Compiegne University of Technology, FRANCE. Between 1999 and 2001 he held a Logan Fellowship in the Department of Engineering and Computer Science at Monash University, Melbourne, Australia. Since July 2001 he has been working in the Department of Engineering, ANU. His research interests are in non-linear control theory with applications in robotics, mechanical systems and motion systems, mathematical systems theory and geometric optimisation techniques with applications in linear algebra, computer vision, digital signal processing and machine learning.

    State estimation and Control for Quadrotor Robotic Vehicles
              —Performance Control of Quadrotor Aerial Robotic Vehicles
    Quadrotor aerial vehicles are one of the most flexible and adaptable platforms for aerial robotics research. The impact of the quadrotor in the field of robotics research can be seen as similar to that of the Puma robotic arm in the early years of robotic manipulators, and the unicycle wheeled robot, in a similarperiod in mobile robotics. This talk presents some of the key ideasbehind state estimation and high performance control for these vehicles.

    2. Prof. Simon Julier, University College London, England
    Simon Julier is a Reader in Situation Awareness Systems in the Department of Computer Science, University College London and holds a DPhil from the Robotics Research Group, the University of Oxford, UK. During his DPhil he assisted Jeff Uhlmann in the development of both the Unscented Kalman Filter and Covariance Intersection fusion algorithms. Between 1997 and 2006, he worked at the Naval Research Laboratory, Washington DC, where he lead a team to develop mobile augmented reality systems. Since 2006 he has been working at UCL where he has been developing algorithms for simultaneous localisation mapping, augmented reality, distributed data fusion. Recently, he has begun to study the implications of the use of negative information (lack of detections) in filtering and estimation algorithms.

    Distributed Multi-Target Fusion of PHD Filters via Exponential Mixture Densities
    In this talk, we consider the problem of Distributed Multi-sensor Multi-target Tracking (DMMT) for networked fusion systems. Many existing approaches for DMMT use multiple hypothesis tracking and track-to-track fusion. However, there are two difficulties with these approaches. First, the computational costs of these algorithms can scale factorially with the number of hypotheses. Second, consistent optimal fusion, which does not double count information, can only be guaranteed for highly constrained network architectures which largely undermine the benefits of distributed fusion.
    In this talk, I will present an approach, developed by Murat Üney and Daniel Clark, together with myself, which attempts to address both problems in a single consistent and coherent framework. Our approach combines two approaches - Random Finite Sets (RFSs) and Exponential Mixture Densities (EMDs) - together. RFSs provide an elegant, uniform framework for multi-target tracking. EMDs provide provably consistent algoritms for fusion. Combining these two components together, we are able to provide a single unified solution for DMMT.