  Cooperative perception is challenging for safety-critical autonomous driving
applications.The errors in the shared position and pose cause an inaccurate
relative transform estimation and disrupt the robust mapping of the Ego
vehicle. We propose a distributed object-level cooperative perception system
called OptiMatch, in which the detected 3D bounding boxes and local state
information are shared between the connected vehicles. To correct the noisy
relative transform, the local measurements of both connected vehicles (bounding
boxes) are utilized, and an optimal transport theory-based algorithm is
developed to filter out those objects jointly detected by the vehicles along
with their correspondence, constructing an associated co-visible set. A
correction transform is estimated from the matched object pairs and further
applied to the noisy relative transform, followed by global fusion and dynamic
mapping. Experiment results show that robust performance is achieved for
different levels of location and heading errors, and the proposed framework
outperforms the state-of-the-art benchmark fusion schemes, including early,
late, and intermediate fusion, on average precision by a large margin when
location and/or heading errors occur.
