Multi-component information-equalized extended strong tracking filter for global localization: A scheme robust to kidnapping and symmetrical environments

  • Authors:

  • Zhibin Liu;Zongying Shi;Wenli Xu

  • Affiliations:

  • Tsinghua National Laboratory for Information Science and Technology, Department of Automation, Tsinghua University, 100084, Beijing, PR China;Tsinghua National Laboratory for Information Science and Technology, Department of Automation, Tsinghua University, 100084, Beijing, PR China;Tsinghua National Laboratory for Information Science and Technology, Department of Automation, Tsinghua University, 100084, Beijing, PR China

  • Venue:
  • Robotics and Autonomous Systems
  • Year:
  • 2010

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Abstract

This paper proposes a novel multi-component information-equalized extended strong tracking filtering (MIST) method for mobile robot global localization. The main contributions of this paper come into three aspects: (1) it is the first time that a strong tracking filter (STF) is introduced into the robotics domain and is extended to be suitable for fusing observations with arbitrarily time-varying dimensionality, based on the proposed extended orthogonality principle and the equivalent space transformation; (2) the information asymmetry problem is analyzed, and an information equalization method based on extracting and equalizing the information underlying the residuals between actual and predicted observations is proposed and integrated with the extended strong tracking filter (ESTF) so as to make it to be equally sensitive to the saltation and estimation-error in any dimension of the state space; (3) a probabilistic data association mechanism and the dynamic multiple component-filters evolving mechanism are proposed and combined with the information-equalized ESTF (IESTF), which results in the final form of the proposed MIST localization method. MIST uses multiple individual IESTFs (component filters) to track multiple probable pose hypotheses. The number of IESTFs is automatically tuned through merging, splitting, deletion and generation, so as to adapt to the time-varying multimodal posterior distribution of the estimated robot's pose. The correctness of each hypothesis (tracked by an individual IESTF) is evaluated based on a probabilistic formulation. The effectiveness of the proposed MIST method has been validated by real robot experiments and compared with the performances of popular existing methods such as MHL and MCL, which shows that MIST has a definite robustness to sensor noises, the kidnapped robot problem, nonlinearity of the system, and symmetric environments, as well as a high convergence speed and computational efficiency.