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.
翻译:本研究为黑盒 3D LiDAR odology 算法提出了一个适应性数据驱动的超参数调整框架。 拟议的框架包括离线参数- 感应函数建模和在线适应参数选择。 在离线步骤中, 我们运行了与不同参数和环境调适的odoric 估计算法, 并评估了估计轨迹的准确性, 以构建一个替代函数, 预测特定参数和环境的轨迹估计错误。 随后, 我们根据对代孕函数的轨迹错误预测, 选择了预期会给特定环境带来良好准确性的参数集。 拟议的框架不需要对算法内部工作进行详细调整的信息, 并且通过调整优化参数集来提高其准确性。 我们首先用基于模拟的图例来展示拟议框架在提高不同环境中的odoricat估计的准确性方面的作用。 此外, 对公共数据集 KITTI 的评估表明, 拟议的框架可以提高实际情况下若干odorication 算法的准确性。