A Comparison of Segmentation Methods for Semantic OctoMap Generation
Semantic mapping plays a critical role in enabling autonomous vehicles to understand and navigate complex environments. Instead of computationally demanding 3D segmentation of point clouds, we propose efficient segmentation on RGB images and projection of the corresponding LIDAR measurements on the...
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MDPI AG
2025-06-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/13/7285 |
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| author | Marcin Czajka Maciej Krupka Daria Kubacka Michał Remigiusz Janiszewski Dominik Belter |
| author_facet | Marcin Czajka Maciej Krupka Daria Kubacka Michał Remigiusz Janiszewski Dominik Belter |
| author_sort | Marcin Czajka |
| collection | DOAJ |
| description | Semantic mapping plays a critical role in enabling autonomous vehicles to understand and navigate complex environments. Instead of computationally demanding 3D segmentation of point clouds, we propose efficient segmentation on RGB images and projection of the corresponding LIDAR measurements on the semantic OctoMap. This study presents a comparative evaluation of different semantic segmentation methods and examines the impact of input image resolution on the accuracy of 3D semantic environment reconstruction, inference time, and computational resource usage. The experiments were conducted using an ROS 2-based pipeline that combines RGB images and LiDAR point clouds. Semantic segmentation is performed using ONNX-exported deep neural networks, with class predictions projected onto corresponding 3D LiDAR data using calibrated extrinsic. The resulting semantically annotated point clouds are fused into a probabilistic 3D representation using an OctoMap, where each voxel stores both occupancy and semantic class information. Multiple encoder–decoder architectures with various backbone configurations are evaluated in terms of segmentation quality, latency, memory footprint, and GPU utilization. Furthermore, a comparison between high and low image resolutions is conducted to assess trade-offs between model accuracy and real-time applicability. |
| format | Article |
| id | doaj-art-d110b75809824d85be6f0a1d3aa17c2e |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-d110b75809824d85be6f0a1d3aa17c2e2025-08-20T02:35:56ZengMDPI AGApplied Sciences2076-34172025-06-011513728510.3390/app15137285A Comparison of Segmentation Methods for Semantic OctoMap GenerationMarcin Czajka0Maciej Krupka1Daria Kubacka2Michał Remigiusz Janiszewski3Dominik Belter4Institute of Robotics and Machine Intelligence, Poznan University of Technology, pl. Marii Sklodowskiej-Curie 5, 60-965 Poznan, PolandInstitute of Robotics and Machine Intelligence, Poznan University of Technology, pl. Marii Sklodowskiej-Curie 5, 60-965 Poznan, PolandInstitute of Robotics and Machine Intelligence, Poznan University of Technology, pl. Marii Sklodowskiej-Curie 5, 60-965 Poznan, PolandInstitute of Robotics and Machine Intelligence, Poznan University of Technology, pl. Marii Sklodowskiej-Curie 5, 60-965 Poznan, PolandInstitute of Robotics and Machine Intelligence, Poznan University of Technology, pl. Marii Sklodowskiej-Curie 5, 60-965 Poznan, PolandSemantic mapping plays a critical role in enabling autonomous vehicles to understand and navigate complex environments. Instead of computationally demanding 3D segmentation of point clouds, we propose efficient segmentation on RGB images and projection of the corresponding LIDAR measurements on the semantic OctoMap. This study presents a comparative evaluation of different semantic segmentation methods and examines the impact of input image resolution on the accuracy of 3D semantic environment reconstruction, inference time, and computational resource usage. The experiments were conducted using an ROS 2-based pipeline that combines RGB images and LiDAR point clouds. Semantic segmentation is performed using ONNX-exported deep neural networks, with class predictions projected onto corresponding 3D LiDAR data using calibrated extrinsic. The resulting semantically annotated point clouds are fused into a probabilistic 3D representation using an OctoMap, where each voxel stores both occupancy and semantic class information. Multiple encoder–decoder architectures with various backbone configurations are evaluated in terms of segmentation quality, latency, memory footprint, and GPU utilization. Furthermore, a comparison between high and low image resolutions is conducted to assess trade-offs between model accuracy and real-time applicability.https://www.mdpi.com/2076-3417/15/13/7285semantic mappingsemantic segmentationautonomous vehicles |
| spellingShingle | Marcin Czajka Maciej Krupka Daria Kubacka Michał Remigiusz Janiszewski Dominik Belter A Comparison of Segmentation Methods for Semantic OctoMap Generation Applied Sciences semantic mapping semantic segmentation autonomous vehicles |
| title | A Comparison of Segmentation Methods for Semantic OctoMap Generation |
| title_full | A Comparison of Segmentation Methods for Semantic OctoMap Generation |
| title_fullStr | A Comparison of Segmentation Methods for Semantic OctoMap Generation |
| title_full_unstemmed | A Comparison of Segmentation Methods for Semantic OctoMap Generation |
| title_short | A Comparison of Segmentation Methods for Semantic OctoMap Generation |
| title_sort | comparison of segmentation methods for semantic octomap generation |
| topic | semantic mapping semantic segmentation autonomous vehicles |
| url | https://www.mdpi.com/2076-3417/15/13/7285 |
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