  Cooperative perception is a promising technique for intelligent and connected
vehicles through vehicle-to-everything (V2X) cooperation, provided that
accurate pose information and relative pose transforms are available.
Nevertheless, obtaining precise positioning information often entails high
costs associated with navigation systems. {Hence, it is required to calibrate
relative pose information for multi-agent cooperative perception.} This paper
proposes a simple but effective object association approach named context-based
matching (CBM), which identifies inter-agent object correspondences using
intra-agent geometrical context. In detail, this method constructs contexts
using the relative position of the detected bounding boxes, followed by local
context matching and global consensus maximization. The optimal relative pose
transform is estimated based on the matched correspondences, followed by
cooperative perception fusion. Extensive experiments are conducted on both the
simulated and real-world datasets. Even with larger inter-agent localization
errors, high object association precision and decimeter-level relative pose
calibration accuracy are achieved among the cooperating agents.
