Inferring the posterior distribution in SLAM is critical for evaluating the uncertainty in localization and mapping, as well as supporting subsequent planning tasks aiming to reduce uncertainty for safe navigation. However, real-time full posterior inference techniques, such as Gaussian approximation and particle filters, either lack expressiveness for representing non-Gaussian posteriors or suffer from performance degeneracy when estimating high-dimensional posteriors. Inspired by the complementary strengths of Gaussian approximation and particle filters$\unicode{x2013}$scalability and non-Gaussian estimation, respectively$\unicode{x2013}$we blend these two approaches to infer marginal posteriors in SLAM. Specifically, Gaussian approximation provides robot pose distributions on which particle filters are conditioned to sample landmark marginals. In return, the maximum a posteriori point among these samples can be used to reset linearization points in the nonlinear optimization solver of the Gaussian approximation, facilitating the pursuit of global optima. We demonstrate the scalability, generalizability, and accuracy of our algorithm for real-time full posterior inference on realworld range-only SLAM and object-based bearing-only SLAM datasets.

翻译：在SLAM中推断后验分布对于评估定位与建图的不确定性至关重要，同时也有助于支持后续旨在降低不确定性以实现安全导航的规划任务。然而，实时全后验推断技术（如高斯近似和粒子滤波）要么缺乏表征非高斯后验的表达能力，要么在估计高维后验时存在性能退化问题。受高斯近似（可扩展性）与粒子滤波（非高斯估计）互补优势的启发，我们融合这两种方法以推断SLAM中的边缘后验。具体而言，高斯近似提供机器人位姿分布，粒子滤波以此分布为条件采样地标边缘分布；作为回报，这些样本中的最大后验点可用于重置高斯近似非线性优化求解器中的线性化点，从而促进全局最优解的搜索。我们通过在真实场景的纯距离SLAM与基于目标的纯方位SLAM数据集上验证了算法在实时全后验推断中的可扩展性、泛化性和精度。