Map Management for Efficient Long-Term Visual Localization in Outdoor Environments

Mathias Buerki, Marcyn Dymczyk, Igor Gilitschenski, Cesar Cadena, Roland Siegwart, and Juan Nieto

IEEE Intelligent Vehicles Symposium (IV) 2018

We present a complete map management process for a visual localization system designed for multi-vehicle long-term operations in resource constrained outdoor environments. Outdoor visual localization generates large amounts of data that need to be incorporated into a lifelong visual map in order to allow localization at all times and under all appearance conditions. Processing these large quantities of data is nontrivial, as it is subject to limited computational and storage capabilities both on the vehicle and on the mapping back-end. We address this problem with a two-fold map update paradigm capable of, either, adding new visual cues to the map, or updating co-observation statistics. The former, in combination with offline map summarization techniques, allows enhancing the appearance coverage of the lifelong map while keeping the map size limited. On the other hand, the latter is able to significantly boost the appearance-based landmark selection for efficient online localization without incurring any additional computational or storage burden. Our evaluation in challenging outdoor conditions shows that our proposed map management process allows building and maintaining maps for precise visual localization over long time spans in a tractable and scalable fashion

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@inproceedings{BuerkiIROS2016,
Title = {Map Management for Efficient Long-Term Visual Localization in Outdoor Environments},
Author = {M. Buerki and M. Dymczyk and I. Gilitschenski and C. Cadena and R. Siegwart and J. Nieto},
Fullauthor = {Mathias Buerki and Marcyn Dymczyk and Igor Gilitschenski and Cesar Cadena and Roland Siegwart and Juan Nieto},
Booktitle = {{IEEE} Intelligent Vehicles Symposium ({IV})},
Month = {June},
Year = {2018},
}

Appearance-Based Landmark Selection for Efficient Long-Term Visual Localization

Mathias Buerki, Igor Gilitschenski, Elena Stumm, Roland Siegwart, and Juan Nieto

International Conference on Intelligent Robots and Systems (IROS) 2016

landmark_selectionWe present an online landmark selection method for efficient and accurate visual localization under changing appearance conditions. The wide range of conditions encountered during long-term visual localization by e.g. fleets of autonomous vehicles offers the potential exploit redundancy and reduce data usage by selecting only those visual cues which are relevant at the given time. Therefore co-observability statistics guide landmark ranking and selection, significantly reducing the amount of information used for localization while maintaining or even improving accuracy.

pdf   video

@inproceedings{BuerkiIROS2016,
Title = {Appearance-Based Landmark Selection for Efficient Long-Term Visual Localization},
Author = {M. Buerki and I. Gilitschenski and E. Stumm and R. Siegwart and J. Nieto},
Fullauthor = {Mathias Buerki and Igor Gilitschenski and Elena Stumm and Roland Siegwart and Juan Nieto},
Booktitle = {{IEEE/RSJ} International Conference on Intelligent Robots and Systems ({IROS})},
Address = {Daejeon, Korea},
Month = {October},
Year = {2016},
}