LOGOS: 仅基于激光雷达的高斯高程喷溅用于统一微小障碍物分割
LOGOS: LiDAR-Only Gaussian Elevation Splatting for Unified Tiny Obstacle Segmentation
AI总结：
提出LOGOS系统，通过将路面建模为2D高斯原语的连续混合，利用无反向传播的激光雷达方法估计高程，实现微小障碍物分割，在道路和越野场景中优于现有方法。
摘要：
鲁棒的障碍物分割对于智能机器人的安全至关重要，其中基于激光雷达的感知系统在机器人-环境交互中扮演基础角色。尽管广泛的基于激光雷达的方法在城市场景的常见障碍物上表现出高性能，但由于微小障碍物与固有道路起伏之间的显著相似性，它们在路缘、碎石和坑洼等微小障碍物上的结果仍不令人满意。此外，当激光雷达扫描在具有挑战性的越野场景中遭受退化时，它们的分割精度甚至会急剧下降。为克服这些瓶颈，我们提出LOGOS，一个仅基于激光雷达的统一微小障碍物分割系统，它将路面建模为2D高斯原语的连续混合，并通过高精度高程估计区分微小障碍物。与依赖迭代RGB训练的现有高斯喷溅方法不同，LOGOS是一种无反向传播的仅激光雷达方法。它通过自由空间感知初始化直接估计高斯参数，利用平滑性约束逐步修剪非道路原语。随后，通过一种新颖的法线感知高程喷溅函数计算逐点有符号距离，确保对平坦和倾斜地形的鲁棒性。我们在从城市移动场景和采矿运输越野环境中收集的高度异构的点云帧基准上评估LOGOS。这些数据实际使用不同的激光雷达传感器获取，并在点密度、地形粗糙度和障碍物类型上表现出巨大差异。在道路和越野场景上的实验表明，LOGOS显著优于其他最先进的方法，特别是在退化点云区域和具有挑战性的越野场景中，同时保持实时效率。
英文摘要：
Robust obstacle segmentation is essential for the safety of intelligent robots, where LiDAR-based perception systems play a fundamental role in the robot-environment interaction. While extensive LiDAR-based approaches have demonstrated high performance on common obstacles in urban scenarios, their results on tiny obstacles such as curbs, gravel, and potholes remain unsatisfactory due to the significant similarity between tiny obstacles and inherent road undulations. Moreover, their segmentation accuracy even deteriorates sharply when the LiDAR scans suffer from degradation in challenging off-road scenes. To overcome these bottlenecks, we propose LOGOS, a LiDAR-only unified tiny obstacle segmentation system, which models the road surface as a continuous mixture of 2D Gaussian primitives and distinguishes tiny obstacles via high-presicion elevation estimation. Unlike existing Gaussian splatting methods that rely on iterative RGB training, LOGOS is a backpropagation-free LiDAR-only approach. It directly estimates Gaussian parameters via a freespace-aware initialization by incrementally pruning non-road primitives using smoothness constraints. Subsequently, pointwise signed distances are computed via a novel normal-aware elevation splatting function, ensuring robustness to both flat and sloped terrains. We evaluate LOGOS on a highly heterogeneous benchmark of point cloud frames collected from urban mobility scenarios and mining haulage off-road environments. These data are practically acquired using different LiDAR sensors and exhibit large variations in point density, terrain roughness, and obstacle types. Experiments on the road and off-road scenes demonstrate that LOGOS significantly outperforms other state-of-the-art methods, particularly in degraded point cloud regions and challenging off-road scenarios, while maintaining real-time efficiency.
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