Efficient structural perception is essential for mapping and autonomous navigation on resource-constrained robots. Existing 3D methods are computationally prohibitive, while traditional 2D geometric approaches lack robustness. This paper presents a lightweight, real-time framework that projects 3D LiDAR data into 2D Bird's-Eye-View (BEV) images to enable efficient detection of structural elements relevant to mapping and navigation. Within this representation, we systematically evaluate several feature extraction strategies, including classical geometric techniques (Hough Transform, RANSAC, and LSD) and a deep learning detector based on YOLO-OBB. The resulting detections are integrated through a spatiotemporal fusion module that improves stability and robustness across consecutive frames. Experiments conducted on a standard mobile robotic platform highlight clear performance trade-offs. Classical methods such as Hough and LSD provide fast responses but exhibit strong sensitivity to noise, with LSD producing excessive segment fragmentation that leads to system congestion. RANSAC offers improved robustness but fails to meet real-time constraints. In contrast, the YOLO-OBB-based approach achieves the best balance between robustness and computational efficiency, maintaining an end-to-end latency (satisfying 10 Hz operation) while effectively filtering cluttered observations in a low-power single-board computer (SBC) without using GPU acceleration. The main contribution of this work is a computationally efficient BEV-based perception pipeline enabling reliable real-time structural detection from 3D LiDAR on resource-constrained robotic platforms that cannot rely on GPU-intensive processing. The source code and pre-trained models are publicly available.

翻译：高效的结构感知对于资源受限机器人上的地图构建与自主导航至关重要。现有3D方法计算开销过高，而传统2D几何方法鲁棒性不足。本文提出一种轻量级实时框架，将3D LiDAR数据投影为2D鸟瞰图像，从而高效检测与地图构建及导航相关的结构元素。在该表示基础上，我们系统评估了多种特征提取策略，包括经典几何技术（霍夫变换、RANSAC和LSD）以及基于YOLO-OBB的深度学习检测器。检测结果通过时空融合模块整合，提升了连续帧间的稳定性与鲁棒性。在标准移动机器人平台上开展的实验清晰揭示了性能权衡：霍夫变换与LSD等经典方法响应迅速，但对噪声高度敏感，其中LSD产生的过度线段碎片化导致系统拥塞；RANSAC鲁棒性更优，但无法满足实时性要求。相比之下，基于YOLO-OBB的方法在鲁棒性与计算效率间达到最佳平衡，在低功耗单板计算机上无需GPU加速即可维持端到端延迟（满足10Hz运行），同时有效过滤杂乱观测。本研究的主要贡献在于提出一种计算高效的基于鸟瞰图的感知流程，使得无法依赖GPU密集型处理的资源受限机器人平台，能够通过3D LiDAR实现可靠的结构实时检测。源代码与预训练模型均已公开。