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Active Localization Strategy for Hypotheses Pruning in Challenging Environments
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AI
DOI：10.1007/s10846-022-01748-4.png)
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摘要
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Robust localization system has proven to be a cornerstone for mobile robot autonomy. Although passive robot localization is a mature field, it still could fail in challenging environments containing symmetries or open spaces. Active localization can fix this issue by allowing the robot to improve pose estimation by choosing specific actions. We propose an active localization strategy for the indoor position tracking problem in challenging environments. The proposed active localization is performed in three steps: (i) cluster the particle cloud with Spectral Clustering (or Kmeans++) algorithm, (ii) search and select the most informative point in a reduced search space, and (iii) execute rotational actions in order to sense the selected point. Hence, a significant number of wrong hypotheses are pruned. We also introduce a novel study that considers evaluates points in spatial neighborhoods all at once, instead of evaluating each cell independently. Simulated experiments show an improvement in robot pose estimation using the proposed strategy. Real-world validation in symmetric and open office-like environment is also presented.
Keyword:
Active localization
Hypotheses pruning
Clustering
SLAM
Particle filter
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J
IF:
2.8
论文数:
3.9K
被引数:
6.9K
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