In semantic visual simultaneous localization and mapping (VSLAM), accurate data association of semantic measurement from visual sensor is very crucial for robot state estimation and scene reconstruction. However, most of the related works assume a simple world for semantic association. It is still a challenge to deal with the ambiguity of data association in a cluttered environment. In this article, we propose a novel approach to reduce the uncertainty of data association via multiple hypothesis Dirichlet process (MHDP). The posterior distribution of data association is inferred by Dirichlet process (DP) first. Ambiguous associations from the distribution are tackled by a hypothesis tree, and hypothesis testing-based ambiguity judgment is then proposed for each object measurement to provide a strategy for branch growing of the hypothesis tree. Moreover, the proposed data association approach is integrated with a geometric featured-based simultaneous localization and mapping (SLAM) system in a tightly coupled way. The qualitative and quantitative evaluation on simulated and real-world public data sets demonstrates the robustness and effectiveness of our approach compared to other data association methods and the state-of-the-art SLAM system.

中文翻译：

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不仅看，而且推断：语义 SLAM 数据关联推理的多重假设聚类


在语义视觉同步定位与建图（VSLAM）中，视觉传感器语义测量的准确数据关联对于机器人状态估计和场景重建至关重要。然而，大多数相关工作都假设语义关联是一个简单的世界。在杂乱的环境中处理数据关联的模糊性仍然是一个挑战。在本文中，我们提出了一种通过多假设狄利克雷过程（MHDP）来减少数据关联的不确定性的新方法。首先通过狄利克雷过程（DP）推断数据关联的后验分布。分布中的模糊关联由假设树解决，然后针对每个对象测量提出基于假设检验的模糊性判断，以提供假设树的分支生长策略。此外，所提出的数据关联方法以紧密耦合的方式与基于几何特征的同步定位和建图（SLAM）系统集成。对模拟和现实世界公共数据集的定性和定量评估表明，与其他数据关联方法和最先进的 SLAM 系统相比，我们的方法具有鲁棒性和有效性。