This work reports a novel multi-frame Bundle Adjustment (BA) framework called RKHS-BA. It uses continuous landmark representations that encode RGB-D/LiDAR and semantic observations in a Reproducing Kernel Hilbert Space (RKHS). With a correspondence-free pose graph formulation, the proposed system constructs a loss function that achieves more generalized convergence than classical point-wise convergence. We demonstrate its applications in multi-view point cloud registration, sliding-window odometry, and global LiDAR mapping on simulated and real data. It shows highly robust pose estimations in extremely noisy scenes and exhibits strong generalization with various types of semantic inputs. The open source implementation is released in https://github.com/UMich-CURLY/RKHS_BA.

翻译：本文提出了一种新颖的多帧光束法平差框架，称为RKHS-BA。该框架利用再生核希尔伯特空间中的连续路标表示，对RGB-D/激光雷达观测数据与语义观测信息进行编码。通过一种无需特征匹配的位姿图构建方法，所提出的系统构建了一个损失函数，该函数比经典的点对点收敛方法实现了更广义的收敛性。我们在仿真和真实数据上展示了其在多视角点云配准、滑动窗口里程计以及全局激光雷达建图中的应用。该系统在极端噪声场景中表现出高度鲁棒的位姿估计能力，并对多种类型的语义输入具有良好的泛化性能。开源实现已发布于 https://github.com/UMich-CURLY/RKHS_BA。