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。现有分割模型在处理细粒度特征方面仍面临挑战，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低下、边界优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优化的可行驶区域语义分割框架AURASeg，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）残差边界优化模块，通过边界辅助特征细化增强边缘刻画能力；（ii）注意力渐进上采样解码器模块，在解码过程中渐进融合多层级特征。此外，我们还整合了（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数据集以及自定义Gazebo室内数据集上的大量实验表明，AURASeg在各项基准测试中均取得显著优势，尤其在边界度量指标上提升明显。最后，我们在Kobuki TurtleBot平台上实现了实时部署，验证了其实用性。相关代码已开源：https://github.com/Narendhiranv04/AURASeg