Closed erblinium closed 1 year ago
Is looks like it is a memory problem. The memory of the ensenso_camera_node
always increases until it crashes with the error code. The virtual camera also disappears from NxView after the crash, and I have to restart machine to get it back.
Investigating further I see that I have another node running of type image_stream
in the ensenso_camera
package. This node's CPU usage increases (up to 100 % :astonished: ) as the nodes run.
Any ideas what might cause this @benthie? I believe it has to do with the requested data from the image_stream
node...
I will have a look at it.
Let me summarize what you are doing: you have an ensenso_camera_node
running and besides that only the image_stream
script?
I will have a look at it.
* Are you using ROS1 or ROS2?
ROS2
* Are you using the virtual camera directly or as a file camera?
Virtual camera directly
Let me summarize what you are doing: you have an
ensenso_camera_node
running and besides that only theimage_stream
script?
Yes, I start them both from a launch file. I also have some other nodes running TF, robot state publishers, but I do not think they cause any problems here.
Thank you for the details. I will get back to you if I have found something .
Which Ensenso SDK version do you use?
Which Ensenso SDK version do you use?
3.3.1417
I think I found a solution for the high CPU load of the python scripts. The fix is to use a MultiThreadedExecutor
in combination with a ReentrantCallbackGroup
. I am currently testing it on my machine and it looks good with regard to the CPU usage. For the memory leak I created a separate issue. I will keep you posted as soon as I have a working fix that also passes all tests.
FYI, the fixes for both this issue and https://github.com/ensenso/ros_driver/issues/107 are part of the currently released v2.1.0.
Getting the following error:
[ERROR] [ensenso_camera_node-10]: process has died [pid 17673, exit code -9
when running theensenso_camera_node
with a virtual camera and virtual objects. It crashes after around ~300-350 seconds.