MAD-ICP: It Is All About Matching Data -- Robust and Informed LiDAR Odometry
<|reference_start|>MAD-ICP: It Is All About Matching Data -- Robust and Informed LiDAR Odometry: LiDAR odometry is the task of estimating the ego-motion of the sensor from sequential laser scans. This problem has been addressed by the community for more than two decades, and many effective solutions are available nowadays. Most of these systems implicitly rely on assumptions about the operating environment, the sensor used, and motion pattern. When these assumptions are violated, several well-known systems tend to perform poorly. This paper presents a LiDAR odometry system that can overcome these limitations and operate well under different operating conditions while achieving performance comparable with domain-specific methods. Our algorithm follows the well-known ICP paradigm that leverages a PCA-based kd-tree implementation that is used to extract structural information about the clouds being registered and to compute the minimization metric for the alignment. The drift is bound by managing the local map based on the estimated uncertainty of the tracked pose. To benefit the community, we release an open-source C++ anytime real-time implementation.<|reference_end|>
@article{ferrari2024mad-icp:,
    title={MAD-ICP: It Is All About Matching Data -- Robust and Informed LiDAR
  Odometry},
    author={Simone Ferrari and Luca Di Giammarino and Leonardo Brizi and Giorgio
  Grisetti},
    journal={arXiv preprint arXiv:2405.05828},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.05828},
    primaryClass={cs.RO cs.CV}
}