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 and structured environments. These difficulties arise from ineffective multi-scale processing, suboptimal boundary refinement, and limited feature representation. To address this, we propose Attention-Guided Upsampling with Residual Boundary-Assistive Refinement (AURASeg), a ground-plane semantic segmentation framework designed to improve border precision while preserving strong region accuracy. Built on a ResNet-50 backbone, AURASeg introduces (i) a Residual Border Refinement Module (RBRM) that enhances edge delineation through boundary-assistive feature refinement, and (ii) Attention Progressive Upsampling Decoder (APUD) blocks that progressively fuse multi-level features during decoding. Additionally, we integrate a (iii) lightweight ASPPLite module to capture multi-scale context with minimal overhead. Extensive experiments on CARL-D, the Ground Mobile Robot Perception (GMRP) dataset, and a custom Gazebo indoor dataset show that AURASeg consistently outperforms strong baselines, with notable gains in boundary metrics. Finally, we demonstrate real-time deployment on a Kobuki TurtleBot, validating practical usability. The code is available at https://github.com/Narendhiranv04/AURASeg

翻译：自由空间地面分割对于自主机器人导航、可行驶区域识别以及高效路径规划至关重要。现有分割模型在细粒度特征处理方面仍面临挑战，尤其在室内及结构化环境中的机器人应用中。这些困难主要源于多尺度处理效率不足、边界细化效果欠佳以及特征表示能力有限。为此，我们提出了一种基于注意力引导上采样与残差边界辅助细化（AURASeg）的地平面语义分割框架，旨在提升边界精度的同时保持优异的区域分割准确性。该框架以ResNet-50为主干网络，创新性地引入：（i）残差边界细化模块（RBRM），通过边界辅助特征细化增强边缘轮廓刻画能力；（ii）注意力渐进上采样解码器（APUD）模块，在解码过程中渐进式融合多层级特征。此外，我们整合了（iii）轻量级ASPPLite模块，以极低计算开销捕获多尺度上下文信息。在CARL-D数据集、地面移动机器人感知（GMRP）数据集及自定义Gazebo室内数据集上的大量实验表明，AURASeg在各项基准测试中均表现优异，尤其在边界相关指标上提升显著。最后，我们在Kobuki TurtleBot平台上实现了实时部署，验证了其实际应用价值。代码已开源：https://github.com/Narendhiranv04/AURASeg