  This study proposes an adaptive data-driven hyperparameter tuning framework
for black-box 3D LiDAR odometry algorithms. The proposed framework comprises
offline parameter-error function modeling and online adaptive parameter
selection. In the offline step, we run the odometry estimation algorithm for
tuning with different parameters and environments and evaluate the accuracy of
the estimated trajectories to build a surrogate function that predicts the
trajectory estimation error for the given parameters and environments.
Subsequently, we select the parameter set that is expected to result in good
accuracy in the given environment based on trajectory error prediction with the
surrogate function. The proposed framework does not require detailed
information on the inner working of the algorithm to be tuned, and improves its
accuracy by adaptively optimizing the parameter set. We first demonstrate the
role of the proposed framework in improving the accuracy of odometry estimation
across different environments with a simulation-based toy example. Further, an
evaluation on the public dataset KITTI shows that the proposed framework can
improve the accuracy of several odometry estimation algorithms in practical
situations.
