Principle:CARLA simulator Carla Multi Sensor Synchronization
| Knowledge Sources | |
|---|---|
| Domains | Simulation, Perception, Sensor_Fusion |
| Last Updated | 2026-02-15 00:00 GMT |
Overview
Multi-sensor synchronization is the technique of ensuring that data collected from multiple sensors within a single simulation tick is temporally aligned and collected in a coordinated manner using a producer-consumer queue pattern.
Description
When running a data collection rig with multiple sensors (cameras, LiDAR, radar, GNSS, IMU), each sensor's listen() callback fires independently on background threads as data becomes available. Without explicit synchronization, the client cannot know when all sensors have delivered their data for a given tick, leading to potential race conditions, incomplete frames, or misaligned data.
The standard synchronization pattern in CARLA combines two mechanisms:
- World.tick() advances the simulation by one frame and returns the frame number. In synchronous mode, this blocks until the server has completed rendering, including all sensor data for that frame.
- Queue.get(timeout) retrieves sensor data from thread-safe queues that were populated by the sensor callbacks. By calling Queue.get() once per sensor after each tick, the client collects exactly one data frame per sensor, all corresponding to the same simulation frame.
The complete pattern is:
- Call World.tick() to advance simulation and trigger sensor data production.
- Call queue.get(timeout) for each sensor queue to consume exactly one data item per sensor.
- Process the collected data (save, transform, fuse).
- Repeat for the next frame.
This guarantees that the client processes a complete, synchronized set of sensor observations before advancing to the next simulation step.
Usage
This pattern is used in every synchronous multi-sensor data collection loop. It is the core mechanism that ties together sensor spawning, callback registration, and data processing into a coherent frame-by-frame pipeline.
Theoretical Basis
Producer-Consumer Pattern: Each sensor callback acts as a producer that places data into a shared buffer (queue), while the main collection loop acts as the consumer that retrieves data. Python's queue.Queue provides thread-safe FIFO semantics with blocking get() calls, making it ideal for this pattern. The queue decouples the asynchronous callback invocations from the synchronous collection loop.
Temporal Alignment Guarantee: In synchronous mode, World.tick() returns only after the server has finished processing the frame, which includes rendering all sensor observations. This means that by the time tick() returns, all sensor callbacks for that frame have either already fired or will fire imminently. The subsequent Queue.get() calls wait for any remaining callbacks, ensuring that all data is collected before proceeding.
Frame Number Consistency: Every SensorData object carries a frame attribute corresponding to the simulation frame that produced it. After collecting data from all queues, the client can verify that all frame numbers match, providing an explicit check on temporal alignment:
assert rgb_data.frame == lidar_data.frame == imu_data.frame
Timeout Handling: The timeout parameter on Queue.get() prevents indefinite blocking if a sensor fails to produce data (e.g., due to a server error or misconfiguration). If the timeout expires, a queue.Empty exception is raised, allowing the client to handle the error gracefully rather than hanging forever.
Bounded vs. Unbounded Queues: Using unbounded queues (the default) is safe in synchronous mode because at most one item is produced per sensor per tick, and the consumer drains the queue before the next tick. In asynchronous mode or if the consumer falls behind, bounded queues (queue.Queue(maxsize=N)) can prevent unbounded memory growth, though the producer (callback) may block if the queue is full.
Scalability: The pattern scales linearly with the number of sensors. For N sensors, each tick requires N Queue.get() calls. The total wall-clock time per tick is dominated by the slowest sensor (typically LiDAR rendering or high-resolution camera rendering) plus queue retrieval overhead, which is negligible.