MAD-ICP: It Is All About Matching Data – Robust and Informed LiDAR Odometry
arxiv(2024)
摘要
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.
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