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 | 1 | initial version |
You are seeing the correct behavior. You can take a look at the REP for Coordinate Frames for more information about the frame conventions.
In a nutshell, the odom frame contains the pose of the robot in the world as reported by some odometry sensor. The odom pose is continuous; however, due to errors in odometry, it accumulates error over time.
To correct for the drift of odometric sensors, packages such as slam_gmapping introduce a transform between map --> odom. This transform is the correction that has been computed, based on sensor input such as laser scanners. It is not continuous (it can jump at various time intervals) because slam_gmapping usually computes corrections at a lower frequency.
If you have perfect odometry sensor, the map --> odom tf will always be 0.
| | 2 | No.2 Revision |
You are seeing the correct behavior. You can take a look at the REP for Coordinate Frames for more information about the frame conventions.
In a nutshell, the odom frame contains the pose of the robot in the world as reported by some odometry sensor. The odom pose is continuous; however, due to errors in odometry, it accumulates error over time.
To correct for the drift of odometric sensors, packages such as slam_gmapping introduce a transform between map --> odom. This transform is the correction that has been computed, based on sensor input such as laser scanners. It is not continuous (it can jump at various time intervals) because slam_gmapping usually computes corrections at a lower frequency.
If you have a perfect odometry sensor, the map --> odom tf will always be 0.
| | 3 | No.3 Revision |
You are seeing the correct behavior. You can take a look at the REP for Coordinate Frames for more information about the frame conventions.
In a nutshell, the odom frame contains the pose of the robot in the world as reported by some odometry sensor. The odom pose is continuous; however, due to errors in odometry, it accumulates error over time.
To correct for the drift of odometric sensors, packages such as slam_gmapping introduce a transform between map --> odom. This transform is the correction that has been computed, based on sensor input such as laser scanners. It is not continuous (it can jump at various time intervals) because slam_gmapping usually computes corrections at a lower frequency.
If you have a perfect odometry sensor, sensor and a "perfect" laser, the map --> odom tf will always be 0.
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