Lukas Schaupp, Mathias Buerki, Renaud Dube, Roland Siegwart, and Cesar Cadena
IEEE/RJS Int. Conference on Intelligent RObots and Systems (IROS) 2019
We introduce a novel method for oriented place recognition with 3D LiDAR scans. A Convolutional Neural Network is trained to extract compact descriptors from single 3D LiDAR scans. These can be used both to retrieve near-by place candidates from a map, and to estimate the yaw discrepancy needed for bootstrapping local registration methods. We employ a triplet loss function for training and use a hard negative mining strategy to further increase the performance of our descriptor extractor. In an evaluation on the NCLT and KITTI datasets, we demonstrate that our method outperforms related state-of-the-art approaches based on both data-driven and handcrafted data representation in challenging long-term outdoor conditions.
@inproceedings{SchauppIROS2019,
Title = {Map Management for Efficient Long-Term Visual Localization in Outdoor Environments},
Author = {L. Schaupp and M. Buerki and R. Dube and R. Siegwart and C. Cadena},
Fullauthor = {Lukas Schaupp and Mathias Buerki and Renaud Dube and Roland Siegwart and Cesar Cadena},
Booktitle = {{IEEE/RJS} Int. Conference on Intelligent RObots and Systems ({IROS})},
Month = {November},
Year = {2019},
}