Humans cannot always be treated as oracles for collaborative sensing. Robots thus need to maintain beliefs over unknown world states when receiving semantic data from humans, as well as account for possible discrepancies between human-provided data and these beliefs. To this end, this paper introduces the problem of semantic data association (SDA) in relation to conventional data association problems for sensor fusion. It then develops a novel probabilistic semantic data association (PSDA) algorithm to rigorously address SDA in general settings, unlike previous work on semantic data fusion which developed heuristic techniques for specific settings. PSDA is further incorporated into a recursive hybrid Bayesian data fusion scheme which uses Gaussian mixture priors for object states and softmax functions for semantic human sensor data likelihoods. Simulations of a multi-object search task show that PSDA enables robust collaborative state estimation under a wide range of conditions where semantic human sensor data can be erroneous or contain significant reference ambiguities.

翻译：人类不能总是被视为协同感知中的“先知”。因此，当从人类接收语义数据时，机器人需要维护对未知世界状态的信念，同时考虑人类提供的数据与这些信念之间可能存在的不一致。为此，本文首先引入了语义数据关联（SDA）问题，并将其与传感器融合中的传统数据关联问题相关联。随后，本文提出了一种新颖的概率语义数据关联（PSDA）算法，以严格解决一般设置下的SDA问题，这不同于以往针对特定设置开发启发式技术的语义数据融合研究。进一步地，PSDA被融入一种递归混合贝叶斯数据融合框架中，该框架使用高斯混合先验表示对象状态，并使用softmax函数表示语义人类传感器数据的似然。对多目标搜索任务的仿真表明，PSDA能够在人类语义传感器数据可能包含错误或显著参考歧义的广泛条件下，实现鲁棒的协同状态估计。