The Masked Mapper: Masked Metric Mapping

Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020

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Abstract

In this paper, we propose a flexible mapping scheme that uses a masking function (mask) to focus the attention of a pose graph SLAM (Simultaneous Localization and Mapping) system. The masking function takes the robot’s observations and returns true if the robot is in an important location. State-of-the-art methods in SLAM generate dense metric lidar maps, creating precise maps at a high computational cost by storing lidar scans for each pose node and continually attempting to close loops. In many cases, trying to always make loop closures is unnecessary for localization and even risky because of perceptual aliasing and false positives. By masking out these less useful positions, our method can create more accurate maps despite performing far fewer scan matches. We evaluate our system with three simple mask functions on a 2.5 km trajectory with significant angular drift. We compare the number of scan matches performed under each mask as well as the accuracy of the loop closures.


bibtex

@inproceedings{haggenmiller2020iros,
    TITLE      = {The Masked Mapper: Masked Metric Mapping},
    AUTHOR     = {Acshi Haggenmiller and Cameron Kabacinski and Maximilian Krogius and
                 Edwin Olson},
    BOOKTITLE  = {Proceedings of the {IEEE/RSJ} International Conference on Intelligent
                 Robots and Systems {(IROS)}},
    YEAR       = {2020},
    MONTH      = { October},
    KEYWORDS   = {Localization, Mapping},
}