Lifelong Information-Driven Exploration to Complete and Refine 4-D Spatio-Temporal Maps

@article{Santos2016LifelongIE,
  title={Lifelong Information-Driven Exploration to Complete and Refine 4-D Spatio-Temporal Maps},
  author={Jo{\~a}o Machado Santos and Tom{\'a}{\vs} Krajn{\'i}k and Jaime Pulido Fentanes and Tom Duckett},
  journal={IEEE Robotics and Automation Letters},
  year={2016},
  volume={1},
  pages={684-691}
}
This letter presents an exploration method that allows mobile robots to build and maintain spatio-temporal models of changing environments. The assumption of a perpetually changing world adds a temporal dimension to the exploration problem, making spatio-temporal exploration a never-ending, life-long learning process. We address the problem by application of information-theoretic exploration methods to spatio-temporal models that represent the uncertainty of environment states as probabilistic… CONTINUE READING

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