基于移动机器人观测的时空人体活动非参数建模
Non-Parametric Modeling of Spatio-Temporal Human Activity Based on Mobile Robot Observations
摘要：
这项工作提出了一个非参数的时空模型映射人类活动的移动自主机器人在长期的背景下。基于变分高斯过程回归，该模型结合了空间和时间周期依赖的先验信息，以创建人类事件的连续表示。由机器人的运动产生的非均匀数据分布通过异方差似然函数被包括在模型中，并且可以被解释为预测不确定性。使用稀疏公式，可以使用多周和数百平方米的数据集来创建模型。基于多周数据集的实验评估表明，所提出的方法在预测质量和后续路径规划方面都优于现有技术。
英文摘要：
This work presents a non-parametric spatio-temporal model for mapping human activity by mobile autonomous robots in a long-term context. Based on Variational Gaussian Process Regression, the model incorporates prior information of spatial and temporal-periodic dependencies to create a continuous representation of human occurrences. The inhomogeneous data distribution resulting from movements of the robot is included in the model via a heteroscedastic likelihood function and can be accounted for as predictive uncertainty. Using a sparse formulation, data sets over multiple weeks and several hundred square meters can be used for model creation. The experimental evaluation, based on multi-week data sets, demonstrates that the proposed approach outperforms the state of the art both in terms of predictive quality and subsequent path planning.
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