Quantitative 3D Map Accuracy Evaluation Hardware and Algorithm for LiDAR(-Inertial) SLAM
<|reference_start|>Quantitative 3D Map Accuracy Evaluation Hardware and Algorithm for LiDAR(-Inertial) SLAM: Accuracy evaluation of a 3D pointcloud map is crucial for the development of autonomous driving systems. In this work, we propose a user-independent software/hardware system that can quantitatively evaluate the accuracy of a 3D pointcloud map acquired from LiDAR(-Inertial) SLAM. We introduce a LiDAR target that functions robustly in the outdoor environment, while remaining observable by LiDAR. We also propose a software algorithm that automatically extracts representative points and calculates the accuracy of the 3D pointcloud map by leveraging GPS position data. This methodology overcomes the limitations of the manual selection method, that its result varies between users. Furthermore, two different error metrics, relative and absolute errors, are introduced to analyze the accuracy from different perspectives. Our implementations are available at: https://github.com/SangwooJung98/3D_Map_Evaluation<|reference_end|>
@article{hahn2024quantitative,
    title={Quantitative 3D Map Accuracy Evaluation Hardware and Algorithm for
  LiDAR(-Inertial) SLAM},
    author={Sanghyun Hahn, Seunghun Oh, Minwoo Jung, Ayoung Kim, Sangwoo Jung},
    journal={arXiv preprint arXiv:2408.09727},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.09727},
    primaryClass={cs.RO}
}