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. 2023 Sep 15;23(18):7915.
doi: 10.3390/s23187915.

Road Descriptors for Fast Global Localization on Rural Roads Using OpenStreetMap

Stephen Ninan  1 , Sivakumar Rathinam  1
Affiliations

Road Descriptors for Fast Global Localization on Rural Roads Using OpenStreetMap

Stephen Ninan et al. Sensors (Basel). .

Abstract

Accurate pose estimation is a fundamental ability that all mobile robots must posses in order to navigate a given environment. Much like a human, this ability is dependent on the robot's understanding of a given scene. For autonomous vehicles (AVs), detailed 3D maps created beforehand are widely used to augment the perceptive abilities and estimate pose based on current sensor measurements. This approach, however, is less suited for rural communities that are sparsely connected and cover large areas. Topological maps such as OpenStreetMap have proven to be a useful alternative in these situations. However, vehicle localization using these maps is non-trivial, particularly for the global localization task, where the map spans large areas. To deal with this challenge, we propose road descriptors along with an initialization technique for localization that allows for fast global pose estimation. We test our algorithms on (real world) maps and benchmark them against other map-based localization as well as SLAM algorithms. Our results show that the proposed method can narrow down the pose to within 50 cm of the ground truth significantly faster than the state-of-the-art methods.

Keywords: OpenStreetMap; descriptors; localization; particle filter; road segmentation.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Comparison between urban and rural scenes.
Figure 2
Figure 2
Generating road descriptors from OpenStreetMap.
Figure 3
Figure 3
Concept of 2-dimensional road descriptors.
Figure 4
Figure 4
Generating road descriptors from segmented point clouds.
Figure 5
Figure 5
Descriptor search process.
Figure 6
Figure 6
Returns from the distance function around a road edge on the map.
Figure 7
Figure 7
Results from LiDAR simulation tests.
Figure 8
Figure 8
Vehicle platform used for real-time testing.
Figure 9
Figure 9
Example of data from the Texas A&M Autonomous Vehicle dataset.
Figure 10
Figure 10
Localization Results for Route 1.
Figure 11
Figure 11
Positional error of localization estimates—Route 2. (1 time step ≈ 0.1 s).
Figure 12
Figure 12
(Left) Search space spanning 36 sq. km, (Right) position estimates after convergence for Route 2.
See this image and copyright information in PMC

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