Automatic Hyper-Parameter Tuning for Black-box LiDAR Odometry

LiDAR odometry algorithms are complex and involve a number of hyper-parameters. The choice of hyper-parameters can substantively affect the performance of odometry estimation, and it is necessary to carefully fine-tune the hyper-parameters depending on the sensor, environment, and algorithm to achieve the best estimation results. While odometry estimation algorithms are often tuned manually, this is time-consuming and may also result in a sub-optimal parameter set. This paper presents an automatic hyper-parameter tuning approach for LiDAR odometry estimation. By taking advantage of the sequential model-based optimization (SMBO) approach, we automatically optimize the hyper-parameter set of a black-box odometry estimation algorithm without detailed knowledge of the algorithm. In addition, a LiDAR data augmentation approach is also proposed to prevent overfitting. Through evaluation, we show that the combination of SMBO-based parameter exploration and data augmentation enables us to efficiently and robustly optimize the hyper-parameter set for several different odometry estimation algorithms. We also demonstrate that the optimized parameter set exhibits superior performance with respect to KITTI dataset and in a real use scenario.