Odometry with lidar sensors is a state-of-the-art method to estimate the ego pose of a moving vehicle. Real-world effects such as dynamic objects, non-overlapping areas, and sensor noise diminish the accuracy of ICP. We build on a recently proposed method that makes these effects visible by visualizing the multidimensional objective function of ICP in two dimensions. We use this method to study different ICP variants in the context of lidar odometry. In addition, we propose a novel method to filter out dynamic objects and to address the ego blind spot problem. Keywords Iterative closest point Registration Odometry Mapping Lidar odometry 1 Introduction Odometry is a widely used technique to estimate the trajectory of a navigating robot or an automated driving system (ADS). As more and more ADSs make their way to higher automation levels, the demand for higher accuracy and robustness rises accordingly. One example of this demand is the use of high-definition maps in such systems. To leverage the map information, the AD system first needs to localize itself in the map. Therefore, the accuracy of the localization influences the overall confidence that can be placed in the map information. Since odometry plays an essential role in many localization systems, the robotics community is interested in improving its accuracy and robustness.

RegHEC is a registration-based hand-eye calibration technique with no need for accurate calibration rig but arbitrary available objects, applicable for both eye-in-hand and eye-to-hand cases. It tries to find the hand-eye relation which brings multi-view point clouds of arbitrary scene into simultaneous registration under a common reference frame. RegHEC first achieves initial alignment of multi-view point clouds via Bayesian optimization, where registration problem is modeled as a Gaussian process over hand-eye relation and the covariance function is modified to be compatible with distance metric in 3-D motion space SE(3), then passes the initial guess of hand-eye relation to an Anderson Accelerated ICP variant for later fine registration and accurate calibration. RegHEC has little requirement on calibration object, it is applicable with sphere, cone, cylinder and even simple plane, which can be quite challenging for correct point cloud registration and sensor motion estimation using existing methods. While suitable for most 3-D vision guided tasks, RegHEC is especially favorable for robotic 3-D reconstruction, as calibration and multi-view point clouds registration of reconstruction target are unified into a single process. Our technique is verified with extensive experiments using varieties of arbitrary objects and real hand-eye system. We release an open-source C++ implementation of RegHEC.