2.5D Layered Sub-Image LIDAR Maps for Autonomous Driving in Multilevel Environments
This paper proposes a reliable framework to map multilevel road structures in the 2D image domain called layered sub-image maps (LSM). The road is divided into a set of sub-areas providing IDs in the XY plane. Each sub-area is decomposed into several layered images using LIDAR intensity and elevatio...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-11-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/14/22/5847 |
| _version_ | 1797464020606779392 |
|---|---|
| author | Mohammad Aldibaja Naoki Suganuma Ryo Yanase |
| author_facet | Mohammad Aldibaja Naoki Suganuma Ryo Yanase |
| author_sort | Mohammad Aldibaja |
| collection | DOAJ |
| description | This paper proposes a reliable framework to map multilevel road structures in the 2D image domain called layered sub-image maps (LSM). The road is divided into a set of sub-areas providing IDs in the XY plane. Each sub-area is decomposed into several layered images using LIDAR intensity and elevation data to form a 2.5D map image. The layered elevation images are given IDs in the Z plane to represent the height of the contained road features in meter-order whereas the elevation pixels indicate the cm-order of the road slope in the range of 200 cm. The layered intensity images are then created to describe the road surface in conjunction with the number of the layered elevation images and the corresponding pixel distributions. A significant map retrieval strategy during autonomous driving has been designed based on the LSM implementation tactic and the IDs in the XYZ plane. The system’s reliability has been proved by a unique localization module to localize an autonomous vehicle in a challenging multilevel environment consisting of four stacked loops with an average accuracy of 5 cm in lateral, longitudinal and altitudinal directions. |
| first_indexed | 2024-03-09T18:02:03Z |
| format | Article |
| id | doaj.art-70c5dd66878b4d03b54b15431e78e66e |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T18:02:03Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-70c5dd66878b4d03b54b15431e78e66e2023-11-24T09:51:33ZengMDPI AGRemote Sensing2072-42922022-11-011422584710.3390/rs142258472.5D Layered Sub-Image LIDAR Maps for Autonomous Driving in Multilevel EnvironmentsMohammad Aldibaja0Naoki Suganuma1Ryo Yanase2The Advanced Mobility Research Institute, Kanazawa University, Kanazawa 920-1192, JapanThe Advanced Mobility Research Institute, Kanazawa University, Kanazawa 920-1192, JapanThe Advanced Mobility Research Institute, Kanazawa University, Kanazawa 920-1192, JapanThis paper proposes a reliable framework to map multilevel road structures in the 2D image domain called layered sub-image maps (LSM). The road is divided into a set of sub-areas providing IDs in the XY plane. Each sub-area is decomposed into several layered images using LIDAR intensity and elevation data to form a 2.5D map image. The layered elevation images are given IDs in the Z plane to represent the height of the contained road features in meter-order whereas the elevation pixels indicate the cm-order of the road slope in the range of 200 cm. The layered intensity images are then created to describe the road surface in conjunction with the number of the layered elevation images and the corresponding pixel distributions. A significant map retrieval strategy during autonomous driving has been designed based on the LSM implementation tactic and the IDs in the XYZ plane. The system’s reliability has been proved by a unique localization module to localize an autonomous vehicle in a challenging multilevel environment consisting of four stacked loops with an average accuracy of 5 cm in lateral, longitudinal and altitudinal directions.https://www.mdpi.com/2072-4292/14/22/5847LIDARmultilevel environments2.5D intensity-elevation mapsautonomous vehiclesmultilayer roads |
| spellingShingle | Mohammad Aldibaja Naoki Suganuma Ryo Yanase 2.5D Layered Sub-Image LIDAR Maps for Autonomous Driving in Multilevel Environments Remote Sensing LIDAR multilevel environments 2.5D intensity-elevation maps autonomous vehicles multilayer roads |
| title | 2.5D Layered Sub-Image LIDAR Maps for Autonomous Driving in Multilevel Environments |
| title_full | 2.5D Layered Sub-Image LIDAR Maps for Autonomous Driving in Multilevel Environments |
| title_fullStr | 2.5D Layered Sub-Image LIDAR Maps for Autonomous Driving in Multilevel Environments |
| title_full_unstemmed | 2.5D Layered Sub-Image LIDAR Maps for Autonomous Driving in Multilevel Environments |
| title_short | 2.5D Layered Sub-Image LIDAR Maps for Autonomous Driving in Multilevel Environments |
| title_sort | 2 5d layered sub image lidar maps for autonomous driving in multilevel environments |
| topic | LIDAR multilevel environments 2.5D intensity-elevation maps autonomous vehicles multilayer roads |
| url | https://www.mdpi.com/2072-4292/14/22/5847 |
| work_keys_str_mv | AT mohammadaldibaja 25dlayeredsubimagelidarmapsforautonomousdrivinginmultilevelenvironments AT naokisuganuma 25dlayeredsubimagelidarmapsforautonomousdrivinginmultilevelenvironments AT ryoyanase 25dlayeredsubimagelidarmapsforautonomousdrivinginmultilevelenvironments |