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Wenli Xu

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5 papers

2 author rows

AAAI Conference 2012 Conference Paper

Approximating the Sum Operation for Marginal-MAP Inference

Qiang Cheng
Feng Chen
Jianwu Dong
Wenli Xu
Alexander Ihler

We study the marginal-MAP problem on graphical models, and present a novel approximation method based on direct approximation of the sum operation. A primary difficulty of marginal-MAP problems lies in the non-commutativity of the sum and max operations, so that even in highly structured models, marginalization may produce a densely connected graph over the variables to be maximized, resulting in an intractable potential function with exponential size. We propose a chain decomposition approach for summing over the marginalized variables, in which we produce a structured approximation to the MAP component of the problem consisting of only pairwise potentials. We show that this approach is equivalent to the maximization of a specific variational free energy, and it provides an upper bound of the optimal probability. Finally, experimental results demonstrate that our method performs favorably compared to previous methods.

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ICRA Conference 2010 Conference Paper

Minimum-error active matching for real-time vision

Zhibin Liu
Zongying Shi
Wenli Xu

As an integral part of real-time vision system, there are two most important requirements for feature matching mechanisms: high computational efficiency for meeting the real-time demands, and high correct matching rate for ensuring the convergence and consistency of state estimation. Both of these are addressed and solved as an integrated whole by the efficient minimum-error active matching scheme proposed in this paper. Image processing is performed in a dynamically guided fashion by checking only parts of the image where positive matches are most probable. For achieving the global consensus matchings, rigorous analysis on how to minimize the matching errors in active matching by choosing an optimal search order is made. After that, practical feature matching algorithms are given, which have naturally absorbed the ideas of nearest neighbor (NN) and joint compatibility branch and bound (JCBB) approaches. Both statistical simulations and real-world experimental results have verified the proposed methods can perform better than the state-of-the-art algorithms, i. e. being able to obtain the best global consensus matchings with much lower computational cost.

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ICRA Conference 2009 Conference Paper

Probabilistic multi-component extended strong tracking filter for mobile robot global localization

Zhibin Liu
Zongying Shi
Wenli Xu

This paper proposes a multi-component extended strong tracking filter (MESTer) for global localization. It is the first time strong tracking filter (STF) is introduced into robotics domain and is fundamentally extended to be suitable for fusing observations with arbitrary time-varying dimensionality, based on equivalent space transformation and extended orthogonality principle. The resulted extended strong tracking filter (ESTF) is then combined with a probabilistic multi-component evolving mechanism and finally forms the MESTer localization method. Real robot experiments and comparisons with existing methods show that MESTer has high convergence speed, computational efficiency and definite robustness to sensor noises, kidnapped robot problem, system nonlinearities, and symmetric environments.

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IROS Conference 2007 Conference Paper

Mobile robots global localization using adaptive dynamic clustered particle filters

Zhibin Liu
Zongying Shi
Mingguo Zhao
Wenli Xu

This article presents an adaptive dynamic clustered particle filtering method for mobile robot global localization. The posterior distribution of robot pose in global localization is usually multimodal due to the symmetry of the environment and ambiguous detected features. Moreover, the multimodal distribution of the posterior varies as the robot moves and observations are obtained. Considering these characteristics, we use a set of clusters of particles to represent the posterior. These clusters are dynamically evolved corresponding to the varying posterior by merging the overlapping clusters and splitting the diffuse clusters or those whose particles gather to some sub-clusters inside. Further, in order to improve computational efficiency without sacrificing estimation accuracy, a mechanism for adapting the sample size of clusters is proposed. The theoretical lower bound of the number of particles needed to limit the estimation error is derived, based on the central limit theorem in multidimensional space and the statistic theory of ImportanceSampling (IS). Simulation results show the effectiveness of the proposed method, which is sufficient to achieve robust tracking of robot’s real pose and meanwhile significantly enhance the computational efficiency.

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IROS Conference 2006 Conference Paper

Decentralized Robust Control of Uncertain Robots with Backlash and Flexibility at Joints

Zongying Shi
Yisheng Zhong
Wenli Xu
Mingguo Zhao

This paper proposes a design method of decentralized robust controllers for robots with joint backlash, flexibility and damping characteristics. For each joint subsystem, a robust tracking controller is designed in two steps: first, a nominal controller is designed for the nominal plant to get desired tracking performance, then a robust compensator is added to restrain the influence of the perturbation, that is the difference of the real plant from the nominal plant. The controller designed by the proposed method is a linear time-invariant one. It is shown that by applying the controller with a sufficiently wide frequency bandwidth robust output tracking property can be achieved in the contact phase while a new contact of the motor with the load in the correct direction is ensured in the backlash phase. An important feature of the method is that the controller parameters can be tuned on-line easily

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