Learning Orientation Field for OSM-Guided Autonomous Navigation
Huang, Yuming Gao, Wei Zhang, Zhiyuan Ghaffari, Maani Song, Dezhen Xu, Chengzhong Kong, Hui
Huang, Yuming
Gao, Wei
Zhang, Zhiyuan
Ghaffari, Maani
Song, Dezhen
Xu, Chengzhong
Kong, Hui
Supervisor
Department
Robotics
Embargo End Date
Type
Journal article
Date
2025
License
Language
English
Collections
Research Projects
Organizational Units
Journal Issue
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.
Citation
Y. Huang et al., “Learning Orientation Field for OSM-Guided Autonomous Navigation,” J Field Robot, 2025, doi: 10.1002/ROB.70060
Source
Journal of Field Robotics
Conference
Keywords
Autonomous Navigation, Openstreetmap, Trajectory Planning
Subjects
Source
Publisher
Wiley