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Towards Accurate Ground Plane Normal Estimation from Ego-Motion
In this paper, we introduce a novel approach for ground plane normal estimation of wheeled vehicles. In practice, the ground plane is dynamically changed due to braking and unstable road surface. As a result, the vehicle pose, especially the pitch angle, is oscillating from subtle to obvious. Thus,...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9741436/ https://www.ncbi.nlm.nih.gov/pubmed/36502078 http://dx.doi.org/10.3390/s22239375 |
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author | Zhang, Jiaxin Sui, Wei Zhang, Qian Chen, Tao Yang, Cong |
author_facet | Zhang, Jiaxin Sui, Wei Zhang, Qian Chen, Tao Yang, Cong |
author_sort | Zhang, Jiaxin |
collection | PubMed |
description | In this paper, we introduce a novel approach for ground plane normal estimation of wheeled vehicles. In practice, the ground plane is dynamically changed due to braking and unstable road surface. As a result, the vehicle pose, especially the pitch angle, is oscillating from subtle to obvious. Thus, estimating ground plane normal is meaningful since it can be encoded to improve the robustness of various autonomous driving tasks (e.g., 3D object detection, road surface reconstruction, and trajectory planning). Our proposed method only uses odometry as input and estimates accurate ground plane normal vectors in real time. Particularly, it fully utilizes the underlying connection between the ego pose odometry (ego-motion) and its nearby ground plane. Built on that, an Invariant Extended Kalman Filter (IEKF) is designed to estimate the normal vector in the sensor’s coordinate. Thus, our proposed method is simple yet efficient and supports both camera- and inertial-based odometry algorithms. Its usability and the marked improvement of robustness are validated through multiple experiments on public datasets. For instance, we achieve state-of-the-art accuracy on KITTI dataset with the estimated vector error of 0.39°. |
format | Online Article Text |
id | pubmed-9741436 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-97414362022-12-11 Towards Accurate Ground Plane Normal Estimation from Ego-Motion Zhang, Jiaxin Sui, Wei Zhang, Qian Chen, Tao Yang, Cong Sensors (Basel) Article In this paper, we introduce a novel approach for ground plane normal estimation of wheeled vehicles. In practice, the ground plane is dynamically changed due to braking and unstable road surface. As a result, the vehicle pose, especially the pitch angle, is oscillating from subtle to obvious. Thus, estimating ground plane normal is meaningful since it can be encoded to improve the robustness of various autonomous driving tasks (e.g., 3D object detection, road surface reconstruction, and trajectory planning). Our proposed method only uses odometry as input and estimates accurate ground plane normal vectors in real time. Particularly, it fully utilizes the underlying connection between the ego pose odometry (ego-motion) and its nearby ground plane. Built on that, an Invariant Extended Kalman Filter (IEKF) is designed to estimate the normal vector in the sensor’s coordinate. Thus, our proposed method is simple yet efficient and supports both camera- and inertial-based odometry algorithms. Its usability and the marked improvement of robustness are validated through multiple experiments on public datasets. For instance, we achieve state-of-the-art accuracy on KITTI dataset with the estimated vector error of 0.39°. MDPI 2022-12-01 /pmc/articles/PMC9741436/ /pubmed/36502078 http://dx.doi.org/10.3390/s22239375 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Zhang, Jiaxin Sui, Wei Zhang, Qian Chen, Tao Yang, Cong Towards Accurate Ground Plane Normal Estimation from Ego-Motion |
title | Towards Accurate Ground Plane Normal Estimation from Ego-Motion |
title_full | Towards Accurate Ground Plane Normal Estimation from Ego-Motion |
title_fullStr | Towards Accurate Ground Plane Normal Estimation from Ego-Motion |
title_full_unstemmed | Towards Accurate Ground Plane Normal Estimation from Ego-Motion |
title_short | Towards Accurate Ground Plane Normal Estimation from Ego-Motion |
title_sort | towards accurate ground plane normal estimation from ego-motion |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9741436/ https://www.ncbi.nlm.nih.gov/pubmed/36502078 http://dx.doi.org/10.3390/s22239375 |
work_keys_str_mv | AT zhangjiaxin towardsaccurategroundplanenormalestimationfromegomotion AT suiwei towardsaccurategroundplanenormalestimationfromegomotion AT zhangqian towardsaccurategroundplanenormalestimationfromegomotion AT chentao towardsaccurategroundplanenormalestimationfromegomotion AT yangcong towardsaccurategroundplanenormalestimationfromegomotion |