Free space ground segmentation is essential to navigate robots and autonomous vehicles, 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. In order to overcome these limitations, we propose Attention-Guided Upsampling with Residual Boundary-Assistive Refinement (AURASeg), a ground-plane semantic segmentation model that maintains high segmentation accuracy while improving border precision. Our method uses CSP-Darknet backbone by adding a Residual Border Refinement Module (RBRM) for accurate edge delineation and an Attention Progressive Upsampling Decoder (APUD) for strong feature integration. We also incorporate a lightweight Atrous Spatial Pyramid Pooling (ASPP-Lite) module to ensure multi-scale context extraction without compromising real-time performance. The proposed model beats benchmark segmentation architectures in mIoU and F1 metrics when tested on the Ground Mobile Robot Perception (GMRP) Dataset and a custom Gazebo indoor dataset. Our approach achieves an improvement in mean Intersection-over-Union (mIoU) of +1.26% and segmentation precision of +1.65% compared to state-of-the-art models. These results show that our technique is feasible for autonomous perception in both indoor and outdoor environments, enabling precise border refinement with minimal effect on inference speed.

翻译：自由空间地面分割对于机器人及自动驾驶车辆的导航、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机制的低效性、边界细化策略的次优性以及特征表示能力的局限性。为克服上述不足，本文提出一种基于注意力引导上采样与残差边界辅助细化（AURASeg）的地面平面语义分割模型，该模型在保持高分割精度的同时显著提升了边界定位准确性。本方法采用CSP-Darknet主干网络，通过引入残差边界细化模块（RBRM）实现精确边缘刻画，并设计注意力渐进上采样解码器（APUD）以强化特征融合能力。同时集成轻量化空洞空间金字塔池化模块（ASPP-Lite），在保证实时性能的前提下实现多尺度上下文特征提取。在Ground Mobile Robot Perception（GMRP）数据集及自定义Gazebo室内数据集上的实验表明，所提模型在mIoU与F1指标上均超越基准分割架构。相较于现有最优模型，本方法在平均交并比（mIoU）上提升1.26%，分割精度提高1.65%。实验结果验证了该技术方案在室内外自主感知场景中的可行性，能够在几乎不影响推理速度的前提下实现精确的边界细化。