ros2 runtime_error ai_generated true

tf2: Transform cache timed out while waiting for transform from 'base_link' to 'odom'

ID: ros2/tf2-timeout-on-startup

Also available as: JSON · Markdown · 中文
88%Fix Rate
84%Confidence
1Evidence
2023-09-05First Seen

Version Compatibility

VersionStatusIntroducedDeprecatedNotes
ROS2 Humble active — — —
ROS2 Iron active — — —
ROS2 Rolling active — — —

Root Cause

The transform broadcaster (e.g., robot_state_publisher or a custom node) is not publishing the required transform frequently enough, or there is a startup delay where the transform is not yet available, causing tf2's cache lookup to timeout after the default duration (e.g., 1 second).

generic

中文

变换广播器（如robot_state_publisher或自定义节点）未足够频繁地发布所需变换，或者存在启动延迟导致变换尚未可用，导致tf2的缓存查找在默认持续时间（如1秒）后超时。

Official Documentation

https://docs.ros.org/en/humble/Tutorials/Intermediate/Tf2/Tf2-Main.html

Workarounds

  1. 90% success Ensure the transform broadcaster is publishing at a high enough rate (e.g., 50 Hz) and that the node's timer for publishing is set correctly. For robot_state_publisher, check that the URDF is loaded and the joint states are being published. 
    Ensure the transform broadcaster is publishing at a high enough rate (e.g., 50 Hz) and that the node's timer for publishing is set correctly. For robot_state_publisher, check that the URDF is loaded and the joint states are being published.
  2. 85% success Add a retry loop with exponential backoff in the subscriber node: 'while not tf_buffer.can_transform('odom', 'base_link', rclpy.time.Time(), timeout=rclpy.duration.Duration(seconds=0.1)): rclpy.spin_once(node)' 
    Add a retry loop with exponential backoff in the subscriber node: 'while not tf_buffer.can_transform('odom', 'base_link', rclpy.time.Time(), timeout=rclpy.duration.Duration(seconds=0.1)): rclpy.spin_once(node)'
  3. 95% success If using a static transform, publish it with a static_transform_publisher in the launch file to ensure it's always available. 
    If using a static transform, publish it with a static_transform_publisher in the launch file to ensure it's always available.

中文步骤

  1. 确保变换广播器以足够高的频率（如50 Hz）发布，并且节点的定时器设置正确。对于robot_state_publisher，检查URDF是否已加载并且关节状态正在发布。
  2. 在订阅者节点中添加带指数退避的重试循环：'while not tf_buffer.can_transform('odom', 'base_link', rclpy.time.Time(), timeout=rclpy.duration.Duration(seconds=0.1)): rclpy.spin_once(node)'
  3. 如果使用静态变换，在启动文件中使用static_transform_publisher发布它，以确保它始终可用。

Dead Ends

Common approaches that don't work:

  1. Increasing the tf2 buffer timeout to a very large value (e.g., 10 seconds) in the node's code 70% fail

    This masks the problem but does not fix the underlying publishing issue; the transform may never arrive if the broadcaster is not running.

  2. Adding a sleep(5) at the beginning of the node's main function to wait for transforms 80% fail

    Sleeping does not guarantee the transform will be available; it only delays the error, and the transform may still timeout.