In multipath-assisted simultaneous localization and mapping (SLAM), position-related information (delays and angles) in multipath components of radio signals is used to simultaneously localize user equipment (UE) and map the environment. Multipath-assisted SLAM often involves unknown parameters that are potentially time varying, such as measurement noise. Knowledge of the measurement noise is of critical importance to multipath-assisted SLAM, and uncertainty in such knowledge will seriously affect estimation accuracy. We address this challenge by improving the belief propagation (BP)-based SLAM algorithm and proposing an adaptive multipath-assisted SLAM algorithm in a Bayesian tracking framework, which enables accommodation of a model mismatch of the measurement noise online. Specifically, we describe the evolution of the measurement noise standard deviation via a Markov chain and integrate it into the factor graph representing the Bayesian model of the multipath-assisted SLAM. Then, the BP message passing algorithm is leveraged to calculate the marginal posterior distributions of the UE, environmental features and the measurement noise standard deviation to achieve SLAM and the adaptive adjustment of the measurement noise. Finally, the experimental results verify the robustness of the proposed adaptive multipath-assisted SLAM algorithm against the uncertainty of the measurement noise.

在多径辅助同时定位和映射 (SLAM) 中，无线电信号多径分量中的位置相关信息（延迟和角度）用于同时定位用户设备 (UE) 和映射环境。多径辅助 SLAM 通常涉及可能随时间变化的未知参数，例如测量噪声。测量噪声的知识对于多径辅助 SLAM 至关重要，此类知识的不确定性将严重影响估计精度。我们通过改进基于置信传播 (BP) 的 SLAM 算法并在贝叶斯跟踪框架中提出自适应多径辅助 SLAM 算法来解决这一挑战，该算法能够在线调节测量噪声的模型失配。具体来说，我们通过马尔可夫链描述测量噪声标准偏差的演变，并将其整合到表示多径辅助 SLAM 贝叶斯模型的因子图中。然后利用BP消息传递算法计算UE的边际后验分布、环境特征和测量噪声标准差，实现SLAM和测量噪声的自适应调整。最后，实验结果验证了所提出的自适应多径辅助 SLAM 算法对测量噪声不确定性的鲁棒性。环境特征和测量噪声标准差，实现SLAM和测量噪声的自适应调整。最后，实验结果验证了所提出的自适应多径辅助 SLAM 算法对测量噪声不确定性的鲁棒性。环境特征和测量噪声标准差，实现SLAM和测量噪声的自适应调整。最后，实验结果验证了所提出的自适应多径辅助 SLAM 算法对测量噪声不确定性的鲁棒性。