This work reports a novel Bundle Adjustment (BA) formulation using a Reproducing Kernel Hilbert Space (RKHS) representation called RKHS-BA. The proposed formulation is correspondence-free, enables the BA to use RGB-D/LiDAR and semantic labels in the optimization directly, and provides a generalization for the photometric loss function commonly used in direct methods. RKHS-BA can incorporate appearance and semantic labels within a continuous spatial-semantic functional representation that does not require optimization via image pyramids. We demonstrate its applications in sliding-window odometry and global LiDAR mapping, which show highly robust performance in extremely challenging scenes and the best trade-off of generalization and accuracy.

翻译：本文提出了一种利用再生核希尔伯特空间（RKHS）表示的新型束调整（BA）公式，称为RKHS-BA。该公式无需寻找对应关系，能够直接使用RGB-D/激光雷达数据和语义标签进行优化，并为直接法中常用的光度损失函数提供了泛化框架。RKHS-BA可将外观与语义标签融入连续的时空-语义函数表示中，无需通过图像金字塔进行优化。我们展示了其在滑动窗口里程计与全局激光雷达建图中的应用，实验表明该方法在极具挑战的场景中展现出高度鲁棒的性能，并达到了泛化能力与精度的最佳平衡。