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Long-term Ground Robot Localization Architecture for Mixed Indoor-Outdoor Scenarios

Javier Pérez-Lara, Fernando Caballero, and Luis Merino. Long-term Ground Robot Localization Architecture for Mixed Indoor-Outdoor Scenarios. In Proceedings of the International Symposium on Robotics, ISR, 2014.

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Abstract

This paper summarizes the validation and experimental results of an architecture for six degree-of-freedom robot localization developed in the framework of the EC funded project FROG (FP7-ICT-2011.2.1). Two main localization issues are considered; one is accuracy, required by the Augmented Reality application, and the second is robustness, in order to achieve long-term autonomy of the robot. The experiments were carried out mainly at the Lisbon Zoo (Portugal), a low GPS visibility area with more than 40,000 square meters and non-planar routes as long as 1 kilometer. The approach considers an offline SLAM and multi-sensor data fusion for map building, and a Rao-Blackwellized filter for online robot localization based on previously computed map. The approach also considers localization failures and provides a method for robot re-localization based on visual place recognition.

BibTeX Entry

@INPROCEEDINGS{isr14,
  author = {Javier P\'{e}rez-Lara and Fernando Caballero and Luis Merino},
  title = {Long-term Ground Robot Localization Architecture for Mixed Indoor-Outdoor Scenarios},
  booktitle = {Proceedings of the International Symposium on Robotics, ISR},
  year = {2014},
   abstract={This paper summarizes the validation and experimental results of an architecture for six degree-of-freedom robot localization developed in the framework of the EC funded project FROG (FP7-ICT-2011.2.1). Two main localization issues are considered; one is accuracy, required by the Augmented Reality application, and the second is robustness, in order to achieve long-term autonomy of the robot. The experiments were carried out mainly at the Lisbon Zoo (Portugal), a low GPS visibility area with more than 40,000 square meters and non-planar routes as long as 1 kilometer. The approach considers an offline SLAM and multi-sensor data fusion for map building, and a Rao-Blackwellized filter for online robot localization based on previously computed map. The approach also considers localization failures and provides a method for robot re-localization based on visual place recognition. },
}

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