2023 Fiscal Year Final Research Report
On Multilevel Road Mapping for Autonomous Vehicles: A Study to Generate Accurate 2.5D LIDAR Maps Using Graph SLAM in Challenging Environments
| Project/Area Number |
22K17974
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| Research Category |
Grant-in-Aid for Early-Career Scientists
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| Allocation Type | Multi-year Fund |
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| Review Section |
Basic Section 61050:Intelligent robotics-related
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| Research Institution | Kanazawa University |
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Principal Investigator |
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| Project Period (FY) |
2022-04-01 – 2024-03-31
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| Keywords | Graph SLAM / Autonomous Vehicles / Mapping Systems / LIDAR |
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| Outline of Final Research Achievements |
Generating precise 2.5D maps in challenging environments was mainly addressed in the first year whereas dealing with multilevel road structures to precisely aligning the layered road context in the global coordinate system was the goal in the second year. Thus, the elevation images have been integrated into the mapping system to indicate the altitudinal values the road surface images. The loop closure module has been modified to detect and distinguish the road layers based on the elevation information automatically. Accordingly, the cost-function was developed to optimize the positions of the road surface images in the XY plane and then minimize the elevation errors at the detected loop-closures and ensure the global map consistency and coherency in XY and Z planes. The cost function has then been modified to combine maps in terms of updating the road surface representation, expanding the encoded areas and adjusting the map global position for precise localization in the real world.
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| Free Research Field |
Autonomous Vehicles
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| Academic Significance and Societal Importance of the Research Achievements |
The proposed mapping system outperformed an accurate GNSS-RTK systems to generate precise 2.5D maps in challenging multilevel environments such as Bejoji and Ohashi junctions and accurately combine maps that collected by different agents to increase the safety and accuracy of autonomous driving
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