Gazebo Ignition recorded ros2 bag drift randomly

Hello Community,

I am using Gazebo ignition simulator to test LIO-SAM ros2 branch now. The problem I met is, LIO-SAM runs well with simulation but when I recorded ros bag from it and play, LIO-SAM performs quite randomly. Usually it starts well but then the drift happens.

I built a simple robot and the lidar and imu both using the gazebo ignition plugins, so the extrinsic Matrix should be right. I have several guess of the possible reasons:

Frame transformation: I have different frame transformation from LIO-SAM's robot, the error logs are also always around 'link transformation'. But there are only imu/data and lidarpoints topics in the rosbag, so it might not be the reason.
Extrinsic Matrix: I'm using unit matrix for both ExtrinsicROT and ExtrinsicRPY. I built a simple robot and the lidar and imu both using the gazebo ignition plugins, so the extrinsic Matrix should be right. The plugin I used for imu is <plugin filename="ignition-gazebo-imu-system" name="ignition::gazebo::systems::Imu"> </plugin> But when I run LIO-SAM in some simulation world, the incremental part of the map shows a rotation trend, so does the trajectory. But in others, there is no such problem.
Time synchronization: Because LIO-SAM always starts well in the beginning and drift later. Meanwhile it ran well with simulation. I guess it's a bad synchronization increasing the error which leads to failure in alignment. But LIO-SAM runs well with ros bags which is recorded in reality with real time. And I checked the timestamp of topics, they look synchronized well.

And it always has this in log, even I decrease the rate of rosbag playing to 0.1: Message Filter dropping message: frame 'vehicle_blue/lidar_link/gpu_lidar' at time 166.800 for reason 'discarding message because the queue is full'

Part of the /imu/data and /points is shown below: `--- header: stamp: sec: 118 nanosec: 782000000 frame_id: vehicle_blue/imu_link/imu_sensor orientation: x: -6.693089243585746e-07 y: 3.1786958765501195e-06 z: -0.00022797704779308332 w: 0.9999999740079578 orientation_covariance:

0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0 angular_velocity: x: 0.00022066749092428036 y: -7.386920544000455e-05 z: 0.00016885955438596516 angular_velocity_covariance:
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0 linear_acceleration: x: 0.01097861728531426 y: 0.0021158993581091067 z: 9.791787718124585 linear_acceleration_covariance:
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0

header: stamp: sec: 118 nanosec: 785000000 frame_id: vehicle_blue/imu_link/imu_sensor orientation: x: -6.740578860418199e-07 y: 3.1719698767112e-06 z: -0.00022797454932045252 w: 0.9999999740085456 orientation_covariance:
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0 angular_velocity: x: -4.3195727250768445e-05 y: -6.625150169082166e-05 z: 9.171380094776304e-05 angular_velocity_covariance:
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0 linear_acceleration: x: -0.005566124718655885 y: -0.01409705211571831 z: 9.773846261730535 linear_acceleration_covariance:
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0

header: stamp: sec: 120 nanosec: 0 frame_id: vehicle_blue/lidar_link/gpu_lidar height: 16 width: 1875 fields:
name: x offset: 0 datatype: 7 count: 1
name: y offset: 4 datatype: 7 count: 1
name: z offset: 8 datatype: 7 count: 1
name: intensity offset: 16 datatype: 7 count: 1
name: ring offset: 24 datatype: 4 count: 1 is_bigendian: false point_step: 32 row_step: 60000 data:
97
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227
192
42
228
143
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121
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243
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'...' is_dense: false

header: stamp: sec: 120 nanosec: 102000000 frame_id: vehicle_blue/lidar_link/gpu_lidar height: 16 width: 1875 fields:
name: x offset: 0 datatype: 7 count: 1
name: y offset: 4 datatype: 7 count: 1
name: z offset: 8 datatype: 7 count: 1
name: intensity offset: 16 datatype: 7 count: 1
name: ring offset: 24 datatype: 4 count: 1 is_bigendian: false point_step: 32 row_step: 60000 data:
58
51
227
192
17
228
143
181
79
131
243
191
0
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188
79
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243
191
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'...' is_dense: false

header: stamp: sec: 120 nanosec: 201000000 frame_id: vehicle_blue/lidar_link/gpu_lidar height: 16 width: 1875 fields:
name: x offset: 0 datatype: 7 count: 1
name: y offset: 4 datatype: 7 count: 1
name: z offset: 8 datatype: 7 count: 1
name: intensity offset: 16 datatype: 7 count: 1
name: ring offset: 24 datatype: 4 count: 1 is_bigendian: false point_step: 32 row_step: 60000 data:
39
54
227
192
236
229
143
181
114
134
243
191
0
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114
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243
191
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'...' is_dense: false

`

Please leave me some thoughts if you met similar situation before or if you have any idea that I should try.

TixiaoShan / LIO-SAM

Gazebo Ignition recorded ros2 bag drift randomly #497

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'...' is_dense: false

'...' is_dense: false

'...' is_dense: false