A Spatial Calibration Method for Robust Cooperative Perception

Understand

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.