Simultaneous localization and mapping (SLAM) is the task of building a map representation of an unknown environment while it at the same time is used for positioning. A probabilistic interpretation of the SLAM task allows for incorporating prior knowledge and for operation under uncertainty. Contrary to the common practice of computing point estimates of the system states, we capture the full posterior density through approximate Bayesian inference. This dynamic learning task falls under state estimation, where the state-of-the-art is in sequential Monte Carlo methods that tackle the forward filtering problem. In this paper, we introduce a framework for probabilistic SLAM using particle smoothing that does not only incorporate observed data in current state estimates, but it also back-tracks the updated knowledge to correct for past drift and ambiguities in both the map and in the states. Our solution can efficiently handle both dense and sparse map representations by Rao-Blackwellization of conditionally linear and conditionally linearized models. We show through simulations and real-world experiments how the principles apply to radio (BLE/Wi-Fi), magnetic field, and visual SLAM. The proposed solution is general, efficient, and works well under confounding noise.

翻译：同时定位与地图构建（SLAM）是一项在未知环境中构建地图表示的同时利用该地图进行定位的任务。对 SLAM 任务的概率解释允许融入先验知识并在不确定性条件下运行。与计算系统状态点估计的常见做法不同，我们通过近似贝叶斯推断来捕获完整的后验密度。这一动态学习任务属于状态估计范畴，当前最先进的方法是处理前向滤波问题的序贯蒙特卡洛方法。在本文中，我们提出了一种基于粒子平滑的概率性 SLAM 框架，该框架不仅将观测数据融入当前状态估计，还能回溯更新后的知识，以修正地图和状态中过去的漂移与歧义。通过将条件线性与条件线性化模型进行 Rao-Blackwellization，我们的解决方案能高效处理稠密和稀疏地图表示。我们通过仿真和真实实验展示了该原理如何应用于无线电（BLE/Wi-Fi）、磁场和视觉 SLAM。所提出的解决方案具有通用性、高效性，且在混杂噪声环境下表现良好。