message-filters

To synchronize ROS 2 data streams by timestamp, use message_filters to create Subscriber instances for your topics, then register a callback with TimeSynchronizer for exact matches or ApproximateTimeSynchronizer for near-simultaneous messages.

Use ApproximateTimeSynchronizer instead of TimeSynchronizer when aligning near-simultaneous ROS 2 messages with slop, which is essential for perception pipelines fusing camera, IMU, lidar, or radar data that lack perfectly matching timestamps.

Yes, message_filters supports multi-sensor synchronization in both Python and C++ ROS 2 nodes, providing usage examples for both subscription patterns alongside QoS considerations and cached filtering for reliable data fusion.

ROS 2 multi-topic synchronization callbacks fail to trigger when timestamp pairing is misaligned or QoS settings mismatch, requiring debugging of message_filters subscriber configurations and QoS profiles to ensure reliable perception pipeline data fusion.

The best way to align camera and IMU data in a ROS 2 perception pipeline is using message_filters to enforce timestamp-based pairing, ensuring synchronized callbacks across sensors using either exact or approximate time matching.