mlsanityData to 13 Oct 2026

AURASeg: Attention-Guided Upsampling with Residual-Assisted Boundary Refinement for Drivable-Area Segmentation

arXiv 2510.21536 ↗First posted 24 Oct 2025cs.RO cs.CVcited by 0

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Abstract (arXiv)

Free-space segmentation is essential for autonomous robots to identify drivable regions and navigate safely across indoor, outdoor, and road-scene environments. However, conventional encoder-decoder models often recover coarse region masks while losing the fine spatial information needed to localize drivable-area boundaries accurately. We propose Attention-Guided Upsampling with Residual-Assisted Boundary Refinement (AURASeg), a segmentation framework designed to preserve region-level accuracy while improving boundary quality. Built on a ResNet-18 encoder, AURASeg introduces an Attention Progressive Upsampling Decoder (APUD) that progressively combines semantic context with high-resolution spatial detail, together with a Residual Boundary Refinement Module (RBRM) that explicitly refines contour-sensitive features before final prediction. We evaluate AURASeg across indoor simulation, ground-robot imagery, and road-driving benchmarks. The results show that our proposed model remains competitive with established segmentation models on region-level metrics while providing particularly strong boundary localization, including in comparison with boundary-focused methods. Detailed ablations further demonstrate the role of the proposed decoding and refinement modules.

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References · 20

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8 more
  1. 2022PIDNet: A Real-time Semantic Segmentation Network Inspired by PID Controllerscited by 84
  2. 2021YOLOP: You Only Look Once for Panoptic Driving Perceptioncited by 82
  3. 2021Boundary-Aware Segmentation Network for Mobile and Web Applicationscited by 41
  4. 2020Self-Supervised Drivable Area and Road Anomaly Segmentation using RGB-D Data for Robotic Wheelchairscited by 18
  5. 2018AUNet: Attention-guided dense-upsampling networks for breast mass segmentation in whole mammogramscited by 15
  6. 2023TwinLiteNet: An Efficient and Lightweight Model for Driveable Area and Lane Segmentation in Self-Driving Carscited by 13
  7. 2023Depth-guided Free-space Segmentation for a Mobile Robotcited by 1
  8. 2024A feature refinement module for light-weight semantic segmentation networkcited by 1

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