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：人类不总是协作感知的权威。因此，当从人类那里接收语义数据时，机器人需要维护对未知世界状态的信仰，并考虑人类提供的数据与这些信仰之间可能存在的差异。为此，该论文介绍了语义数据关联（SDA）问题，并将其与传感器融合中的传统数据关联问题联系起来。然后，开发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，而不是先前在特定设置下开发特定技术的语义数据融合。将PSDA 进一步纳入递归混合贝叶斯数据融合方案中，使用高斯混合先验项进行物体状态估计，使用softmax函数进行语义人类传感器数据似然性估计。在多个对象搜索任务的模拟中，PSDA能够在语义人类传感器数据可能出现错误或含有重大参考模糊性的广泛条件下实现稳健的协作状态估计。