Analysis of Occlusion Effects for Map-Based Self-Localization in Urban Areas
A high-definition (HD) map provides structural information for map-based self-localization, enabling stable estimation in real environments. In urban areas, there are many obstacles, such as buses, that occlude sensor observations, resulting in self-localization errors. However, most of the existing...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-07-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/15/5196 |
| _version_ | 1797411209923788800 |
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| author | Yuki Endo Ehsan Javanmardi Shunsuke Kamijo |
| author_facet | Yuki Endo Ehsan Javanmardi Shunsuke Kamijo |
| author_sort | Yuki Endo |
| collection | DOAJ |
| description | A high-definition (HD) map provides structural information for map-based self-localization, enabling stable estimation in real environments. In urban areas, there are many obstacles, such as buses, that occlude sensor observations, resulting in self-localization errors. However, most of the existing HD map-based self-localization evaluations do not consider sudden significant errors due to obstacles. Instead, they evaluate this in terms of average error over estimated trajectories in an environment with few occlusions. This study evaluated the effects of self-localization estimation on occlusion with synthetically generated obstacles in a real environment. Various patterns of synthetic occlusion enabled the analyses of the effects of self-localization error from various angles. Our experiments showed various characteristics that locations susceptible to obstacles have. For example, we found that occlusion in intersections tends to increase self-localization errors. In addition, we analyzed the geometrical structures of a surrounding environment in high-level error cases and low-level error cases with occlusions. As a result, we suggested the concept that the real environment should have to achieve robust self-localization under occlusion conditions. |
| first_indexed | 2024-03-09T04:42:44Z |
| format | Article |
| id | doaj.art-26f3dc333f464ee4a60ba1aac86e5d06 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T04:42:44Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-26f3dc333f464ee4a60ba1aac86e5d062023-12-03T13:19:04ZengMDPI AGSensors1424-82202021-07-012115519610.3390/s21155196Analysis of Occlusion Effects for Map-Based Self-Localization in Urban AreasYuki Endo0Ehsan Javanmardi1Shunsuke Kamijo2Department of Information & Communication Engineering, Graduate School of Information Science and Technology, The University of Tokyo, Tokyo 153-8505, JapanThe Institute of Industrial Science (IIS), The University of Tokyo, Tokyo 153-8505, JapanThe Institute of Industrial Science (IIS), The University of Tokyo, Tokyo 153-8505, JapanA high-definition (HD) map provides structural information for map-based self-localization, enabling stable estimation in real environments. In urban areas, there are many obstacles, such as buses, that occlude sensor observations, resulting in self-localization errors. However, most of the existing HD map-based self-localization evaluations do not consider sudden significant errors due to obstacles. Instead, they evaluate this in terms of average error over estimated trajectories in an environment with few occlusions. This study evaluated the effects of self-localization estimation on occlusion with synthetically generated obstacles in a real environment. Various patterns of synthetic occlusion enabled the analyses of the effects of self-localization error from various angles. Our experiments showed various characteristics that locations susceptible to obstacles have. For example, we found that occlusion in intersections tends to increase self-localization errors. In addition, we analyzed the geometrical structures of a surrounding environment in high-level error cases and low-level error cases with occlusions. As a result, we suggested the concept that the real environment should have to achieve robust self-localization under occlusion conditions.https://www.mdpi.com/1424-8220/21/15/5196autonomous drivingself-localizationLiDARpoint cloud mapurbanocclusion |
| spellingShingle | Yuki Endo Ehsan Javanmardi Shunsuke Kamijo Analysis of Occlusion Effects for Map-Based Self-Localization in Urban Areas Sensors autonomous driving self-localization LiDAR point cloud map urban occlusion |
| title | Analysis of Occlusion Effects for Map-Based Self-Localization in Urban Areas |
| title_full | Analysis of Occlusion Effects for Map-Based Self-Localization in Urban Areas |
| title_fullStr | Analysis of Occlusion Effects for Map-Based Self-Localization in Urban Areas |
| title_full_unstemmed | Analysis of Occlusion Effects for Map-Based Self-Localization in Urban Areas |
| title_short | Analysis of Occlusion Effects for Map-Based Self-Localization in Urban Areas |
| title_sort | analysis of occlusion effects for map based self localization in urban areas |
| topic | autonomous driving self-localization LiDAR point cloud map urban occlusion |
| url | https://www.mdpi.com/1424-8220/21/15/5196 |
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