A Visual Method to study the Error Function of ICP Algorithms This paper proposes a novel method to study the error function of ICP based algorithms. With our method, we visualize the multidimensional error function of ICP algorithms in one dimension which allows us to compare and quantify the performance of ICP algorithms in an intuitive and descriptive manner. This is motivated by the fact that there are many ICP variants around and for researchers and engineers it is challenging which algorithm they shall choose. New approaches are often only evaluated based on runtime and accuracy. Our visual method allows to gain further insights beyond those metrics. We demonstrate the capability of our method by applying it to the KITTI LIDAR odometry benchmark. Our experiments show evidence for errors in the ground truth data, difficulties in highway scenarios and prove the power of superior error metrics such as the newly emerged symmetric objective function.

2D Range Flowbased Odometry fusing LiDAR and IMU This paper describes a 2D odometry method fusing LiDAR and IMU data to estimate ground vehicle motion and performing better in long corridor scenarios and moving objecting scenarios. The method builds on range flow constraint for every scanned point and rejects moving objects by fusing IMU data. Besides, our method applies the graphical model to solve the optimization problem that adds IMU constraint to the odometry system. Experiment results demonstrate that our approach is able to perform as well as other stateofart approaches and better than these approaches in long corridor scenarios and moving objecting scenarios. Besides we test our approach on an embedded system which makes it suitable for those lowcost robotic applications that require planar odometry.