无需对应点，快速实现点云配准，提升导航与物体识别精度。
Generalized-CVO: Fast and Correspondence-Free Local Point Cloud Registration with Second Order Riemannian Optimization
- 用各向异性核函数表示局部几何，优化表面法向对齐。
- 采用二阶黎曼优化，速度比一阶方法快10倍，误差降低超55%。
- 适合低特征环境下的激光雷达/深度相机跟踪，尤其适合粗初始化后的精修。
我们提出一种快速且无需对应点的局部点云配准方法，利用几何表面结构和再生核希尔伯特空间（RKHS）嵌入。该方法将点云表示为带点级各向异性核的连续函数，以增强沿表面法线方向的对齐，同时放松切向方向的约束。针对由此产生的配准问题，我们设计了一种基于近似黎曼海森矩阵的二阶流形优化方案，在保持精度的同时，相较先前无对应点的RKHS方法提升了高达10倍的速度。在多种室内外数据集上的帧间激光雷达与RGB-D跟踪实验中，均验证了性能提升。在驾驶场景的激光雷达配准任务中，于特征稀疏环境中实现了超过55%的平移与旋转漂移降低。在物体配准基准测试中，相比ICP方法更具鲁棒性，并在中等误对齐条件下，通过全局初始化后进一步提升效果。
原文摘要 · Abstract (English)
We propose a fast and correspondence-free local point cloud registration method that leverages geometric surface structure and reproducing kernel Hilbert space (RKHS) embeddings. The method represents point clouds as continuous functions with point-wise anisotropic kernels that encode local geometry. This formulation improves alignment along surface normals while relaxing alignment along tangential directions. To solve the resulting registration problem, we propose a second-order on-manifold optimization scheme with approximate Riemannian Hessians, achieving a speedup of up to 10x over the first-order solvers used in prior correspondence-free RKHS-based methods. We demonstrate improved frame-to-frame LiDAR and RGB-D tracking accuracy across diverse indoor and outdoor datasets. On a LiDAR tracking registration task in the driving domain, we achieve a reduction of $>55\%$ in both translational and rotational drift in challenging feature-sparse environments. On object registration benchmarks, we show improved robustness over ICP-based methods and further gains when refining global initialization, particularly under moderate misalignment.
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