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Main Authors: Dingler, Sebastian, Burrichter, Hannes
Format: Preprint
Published: 2025
Subjects:
Online Access:https://arxiv.org/abs/2511.14919
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author Dingler, Sebastian
Burrichter, Hannes
author_facet Dingler, Sebastian
Burrichter, Hannes
contents Odometry with lidar sensors is a state-of-the-art method to estimate the ego pose of a moving vehicle. Many implementations of lidar odometry use variants of the Iterative Closest Point (ICP) algorithm. 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.
format Preprint
id arxiv_https___arxiv_org_abs_2511_14919
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle A visual study of ICP variants for Lidar Odometry
Dingler, Sebastian
Burrichter, Hannes
Robotics
Odometry with lidar sensors is a state-of-the-art method to estimate the ego pose of a moving vehicle. Many implementations of lidar odometry use variants of the Iterative Closest Point (ICP) algorithm. 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.
title A visual study of ICP variants for Lidar Odometry
topic Robotics
url https://arxiv.org/abs/2511.14919