BPDA-GMM：基于高斯混合模型的贝叶斯概率数据关联用于语义SLAM
BPDA-GMM: Bayesian Probabilistic Data Association via Gaussian Mixture Models for Semantic SLAM
AI总结：
提出BPDA-GMM在线贝叶斯概率数据关联框架，通过狄利克雷过程先验和中文餐馆过程模型实现语义SLAM中增长式地标关联，并利用α散度退火处理模糊关联，提升轨迹精度和语义建图鲁棒性。
摘要：
概率数据关联（PDA）在感知混淆场景中改进了语义SLAM，但现有方法通常假设固定的地标集、随着地图增长重新计算关联权重，或依赖手动调整的零假设权重。为解决这些限制，我们提出了 extbf{BPDA-GMM}，一个用于具有增长式对象级地图的语义SLAM的在线贝叶斯PDA框架。BPDA-GMM使用狄利克雷过程先验来诱导中文餐馆过程（CRP）关联模型，其中累积证据倾向于已有地标，而浓度参数将概率质量分配给新地标。对于每个语义检测，通过联合语义-几何门选择合理候选，计算CRP加权的关联概率，并以闭合形式将对象地标更新为语义高斯。所得地标集形成高斯混合模型，其主导分量作为最大混合语义因子传递给后端。当关联权重不确定时，一个由模糊触发的$α$-散度退火步骤提高了区分度。最后，解耦的后端将语义因子的位姿雅可比置零，使得噪声检测能够细化地标而不直接扰动轨迹。在仿真和真实室内数据集上的实验表明，与最先进的基线相比，轨迹精度、语义建图质量以及对感知混淆和分类器错误的鲁棒性均有所提升。代码和视频公开于https://github.com/thanhnguyencanh/BPDA-SLAM。
英文摘要：
Probabilistic data association (PDA) improves semantic SLAM in perceptually aliased scenes, but existing methods often assume a fixed landmark set, recompute association weights as the map grows, or rely on hand-tuned null-hypothesis weights. To address these limitations, we propose \textbf{BPDA-GMM}, an online Bayesian PDA framework for semantic SLAM with a growing object-level map. BPDA-GMM uses a Dirichlet-process prior to induce a Chinese Restaurant Process (CRP) association model, where accumulated evidence favors existing landmarks, and the concentration parameter assigns probability mass to new landmarks. For each semantic detection, plausible candidates are selected by a joint semantic-geometric gate, CRP-weighted association probabilities are computed, and object landmarks are updated as semantic Gaussians in closed form. The resulting landmark set forms a Gaussian mixture model, and its dominant component is passed to the back-end as a max-mixture semantic factor. When association weights are inconclusive, an ambiguity-triggered $α$-divergence tempering step improves discrimination. Finally, a decoupled back-end zeroes the pose Jacobian of semantic factors, allowing noisy detections to refine landmarks without directly perturbing the trajectory. Experiments in simulation and on a real indoor dataset demonstrate improved trajectory accuracy, semantic mapping quality, and robustness to perceptual aliasing and classifier errors over state-of-the-art baselines. Code and video are publicly available at https://github.com/thanhnguyencanh/BPDA-SLAM.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
