  Collaborative object localization aims to collaboratively estimate locations
of objects observed from multiple views or perspectives, which is a critical
ability for multi-agent systems such as connected vehicles. To enable
collaborative localization, several model-based state estimation and
learning-based localization methods have been developed. Given their
encouraging performance, model-based state estimation often lacks the ability
to model the complex relationships among multiple objects, while learning-based
methods are typically not able to fuse the observations from an arbitrary
number of views and cannot well model uncertainty. In this paper, we introduce
a novel spatiotemporal graph filter approach that integrates graph learning and
model-based estimation to perform multi-view sensor fusion for collaborative
object localization. Our approach models complex object relationships using a
new spatiotemporal graph representation and fuses multi-view observations in a
Bayesian fashion to improve location estimation under uncertainty. We evaluate
our approach in the applications of connected autonomous driving and multiple
pedestrian localization. Experimental results show that our approach
outperforms previous techniques and achieves the state-of-the-art performance
on collaboration localization.
