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Learning Orientation Field for OSM‐Guided Autonomous Navigation.

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Title: Learning Orientation Field for OSM‐Guided Autonomous Navigation.
Authors: Huang, Yuming1 (AUTHOR), Gao, Wei1 (AUTHOR), Zhang, Zhiyuan2 (AUTHOR), Ghaffari, Maani3,4 (AUTHOR), Song, Dezhen5 (AUTHOR), Xu, Cheng‐Zhong1 (AUTHOR), Kong, Hui1 (AUTHOR) huikong@um.edu.mo
Source: Journal of Field Robotics. Mar2026, Vol. 43 Issue 2, p717-738. 22p.
Subjects: Robotic path planning, Digital maps, Trajectory optimization, LIDAR, Artificial neural networks, Parametric equations
Abstract: OpenStreetMap (OSM) has gained popularity recently in autonomous navigation due to its public accessibility, lower maintenance costs, and broader geographical coverage. However, existing methods often struggle with noisy OSM data and incomplete sensor observations, leading to inaccuracies in trajectory planning. These challenges are particularly evident in complex driving scenarios, such as at intersections or facing occlusions. To address these challenges, we propose a robust and explainable two‐stage framework to learn an Orientation Field (OrField) for robot navigation by integrating LiDAR scans and OSM routes. In the first stage, we introduce a novel representation, OrField, which can provide orientations for each grid on the map, reasoning jointly from noisy LiDAR scans and OSM routes. To generate a robust OrField, we train a deep neural network by encoding a versatile initial OrField and output an optimized OrField. Based on OrField, we propose two trajectory planners for OSM‐guided robot navigation, called Field‐RRT* and Field‐Bezier, respectively, in the second stage by improving the Rapidly Exploring Random Tree (RRT) algorithm and Bezier curve to estimate the trajectories. Thanks to the robustness of OrField which captures both global and local information, Field‐RRT* and Field‐Bezier can generate accurate and reliable trajectories even in challenging conditions. We validate our approach through experiments on the SemanticKITTI data set and our own campus data set. The results demonstrate the effectiveness of our method, achieving superior performance in complex and noisy conditions. Our code for network training and real‐world deployment is available at https://github.com/IMRL/OriField. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Field Robotics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
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PubTypeId: academicJournal
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Items – Name: Title
  Label: Title
  Group: Ti
  Data: Learning Orientation Field for OSM‐Guided Autonomous Navigation.
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  Data: <searchLink fieldCode="AR" term="%22Huang%2C+Yuming%22">Huang, Yuming</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Gao%2C+Wei%22">Gao, Wei</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Zhiyuan%22">Zhang, Zhiyuan</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ghaffari%2C+Maani%22">Ghaffari, Maani</searchLink><relatesTo>3,4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Song%2C+Dezhen%22">Song, Dezhen</searchLink><relatesTo>5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Xu%2C+Cheng‐Zhong%22">Xu, Cheng‐Zhong</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kong%2C+Hui%22">Kong, Hui</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> huikong@um.edu.mo</i>
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Field+Robotics%22">Journal of Field Robotics</searchLink>. Mar2026, Vol. 43 Issue 2, p717-738. 22p.
– Name: Subject
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  Data: <searchLink fieldCode="DE" term="%22Robotic+path+planning%22">Robotic path planning</searchLink><br /><searchLink fieldCode="DE" term="%22Digital+maps%22">Digital maps</searchLink><br /><searchLink fieldCode="DE" term="%22Trajectory+optimization%22">Trajectory optimization</searchLink><br /><searchLink fieldCode="DE" term="%22LIDAR%22">LIDAR</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Parametric+equations%22">Parametric equations</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: OpenStreetMap (OSM) has gained popularity recently in autonomous navigation due to its public accessibility, lower maintenance costs, and broader geographical coverage. However, existing methods often struggle with noisy OSM data and incomplete sensor observations, leading to inaccuracies in trajectory planning. These challenges are particularly evident in complex driving scenarios, such as at intersections or facing occlusions. To address these challenges, we propose a robust and explainable two‐stage framework to learn an Orientation Field (OrField) for robot navigation by integrating LiDAR scans and OSM routes. In the first stage, we introduce a novel representation, OrField, which can provide orientations for each grid on the map, reasoning jointly from noisy LiDAR scans and OSM routes. To generate a robust OrField, we train a deep neural network by encoding a versatile initial OrField and output an optimized OrField. Based on OrField, we propose two trajectory planners for OSM‐guided robot navigation, called Field‐RRT* and Field‐Bezier, respectively, in the second stage by improving the Rapidly Exploring Random Tree (RRT) algorithm and Bezier curve to estimate the trajectories. Thanks to the robustness of OrField which captures both global and local information, Field‐RRT* and Field‐Bezier can generate accurate and reliable trajectories even in challenging conditions. We validate our approach through experiments on the SemanticKITTI data set and our own campus data set. The results demonstrate the effectiveness of our method, achieving superior performance in complex and noisy conditions. Our code for network training and real‐world deployment is available at https://github.com/IMRL/OriField. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Field Robotics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1002/rob.70060
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 22
        StartPage: 717
    Subjects:
      – SubjectFull: Robotic path planning
        Type: general
      – SubjectFull: Digital maps
        Type: general
      – SubjectFull: Trajectory optimization
        Type: general
      – SubjectFull: LIDAR
        Type: general
      – SubjectFull: Artificial neural networks
        Type: general
      – SubjectFull: Parametric equations
        Type: general
    Titles:
      – TitleFull: Learning Orientation Field for OSM‐Guided Autonomous Navigation.
        Type: main
  BibRelationships:
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      – PersonEntity:
          Name:
            NameFull: Huang, Yuming
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          Name:
            NameFull: Gao, Wei
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            NameFull: Zhang, Zhiyuan
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            NameFull: Ghaffari, Maani
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            NameFull: Song, Dezhen
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            NameFull: Xu, Cheng‐Zhong
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            NameFull: Kong, Hui
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          Dates:
            – D: 01
              M: 03
              Text: Mar2026
              Type: published
              Y: 2026
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            – Type: issn-print
              Value: 15564959
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              Value: 43
            – Type: issue
              Value: 2
          Titles:
            – TitleFull: Journal of Field Robotics
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