Adaptive Hyperparameter Tuning for Black-box LiDAR Odometry
This work addresses the challenge of optimizing hyperparameters for LiDAR odometry in robotics and autonomous vehicles, but it is incremental as it builds on existing tuning methods without introducing a new paradigm.
The study tackled the problem of tuning hyperparameters for black-box LiDAR odometry algorithms by proposing an adaptive data-driven framework that uses offline modeling and online selection to predict trajectory errors. The result showed improved accuracy in odometry estimation, as demonstrated on the KITTI dataset with several algorithms.
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.