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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Accesso online: | https://arxiv.org/abs/2510.21536 |
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| _version_ | 1866908869782929408 |
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| author | Vijayakumar, Narendhiran M, Sridevi. |
| author_facet | Vijayakumar, Narendhiran M, Sridevi. |
| contents | Free space ground segmentation is essential to navigate autonomous robots, recognize drivable zones, and traverse efficiently. Fine-grained features remain challenging for existing segmentation models, particularly for robots in indoor, outdoor and road-scene environments. These difficulties arise from ineffective multi-scale processing, sub-optimal boundary refinement, and limited feature representation. To address this, we propose Attention-guided Upsampling with Residual-Assistive Boundary Refinement (AURASeg), a ground-plane drivable area segmentation framework designed to improve boundary precision while preserving strong region accuracy under edge-deployment constraints. Built on ResNet backbone, we propose (i) a Residual Boundary Refinement Module (RBRM) that enhances edge delineation through boundary-assistive feature refinement, and (ii) Attention Progressive Upsampling Decoder (APUD) blocks that fuse multi-level features using residual fusion of attention modules; additionally, we integrate (iii) a lightweight ASPPLite module to capture multi-scale context with minimal overhead. Extensive experiments on CARL-D, the Ground Mobile Robot Perception (GMRPD) dataset, and a custom Gazebo indoor dataset show that AURASeg consistently outperforms strong baselines, with notable gains in boundary metrics. Finally, we demonstrate on-device deployment on a Jetson Nano powered Kobuki TurtleBot, validating practical edge-inference feasibility. Code is omitted for anonymity and will be released upon acceptance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_21536 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AURASeg: Attention-guided Upsampling with Residual-Assistive Boundary Refinement for Onboard Robot Drivable-Area Segmentation Vijayakumar, Narendhiran M, Sridevi. Robotics Computer Vision and Pattern Recognition Free space ground segmentation is essential to navigate autonomous robots, recognize drivable zones, and traverse efficiently. Fine-grained features remain challenging for existing segmentation models, particularly for robots in indoor, outdoor and road-scene environments. These difficulties arise from ineffective multi-scale processing, sub-optimal boundary refinement, and limited feature representation. To address this, we propose Attention-guided Upsampling with Residual-Assistive Boundary Refinement (AURASeg), a ground-plane drivable area segmentation framework designed to improve boundary precision while preserving strong region accuracy under edge-deployment constraints. Built on ResNet backbone, we propose (i) a Residual Boundary Refinement Module (RBRM) that enhances edge delineation through boundary-assistive feature refinement, and (ii) Attention Progressive Upsampling Decoder (APUD) blocks that fuse multi-level features using residual fusion of attention modules; additionally, we integrate (iii) a lightweight ASPPLite module to capture multi-scale context with minimal overhead. Extensive experiments on CARL-D, the Ground Mobile Robot Perception (GMRPD) dataset, and a custom Gazebo indoor dataset show that AURASeg consistently outperforms strong baselines, with notable gains in boundary metrics. Finally, we demonstrate on-device deployment on a Jetson Nano powered Kobuki TurtleBot, validating practical edge-inference feasibility. Code is omitted for anonymity and will be released upon acceptance. |
| title | AURASeg: Attention-guided Upsampling with Residual-Assistive Boundary Refinement for Onboard Robot Drivable-Area Segmentation |
| topic | Robotics Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2510.21536 |