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.

翻译：高效的结构感知对于资源受限机器人的地图构建与自主导航至关重要。现有3D方法计算代价高昂，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激光雷达数据投影至二维鸟瞰图（BEV）中，以实现与建图和导航相关结构元素的高效检测。在该表示框架下，我们系统评估了多种特征提取策略，包括经典几何方法（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进行集成，增强了连续帧之间的稳定性与鲁棒性。在标准移动机器人平台上的实验揭示了清晰的性能权衡：霍夫变换与LSD等经典方法响应迅速，但对噪声高度敏感——LSD产生的线段过度碎片化导致系统阻塞；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取得最佳平衡，可维持端到端延迟（满足10Hz运行要求），同时在无需GPU加速的低功耗单板计算机（SBC）上有效滤除杂乱观测。本研究的主要贡献在于提出一种计算高效的基于BEV的感知流水线，使资源受限、无法依赖GPU密集型处理的机器人平台能够从3D激光雷达实现可靠的实时结构检测。