Frames of Reference Convention¶
Currently the SLAMcore frames of reference do not abide to the ROS frames of reference convention, as defined in REP-105. If this is important to your application, consider using a static tranform publisher node to republish the measurements of the ROS Wrapper using a frame that does abide to REP-105.
When using the RealSense D435i camera by default, our system is configured to track the reference frame that’s rigidly attached to the RealSense IMU chip. With the camera parallel to the ground and facing forwards, that coordinate system has the Z axis pointing forwards and the X axis pointing to the right side.
We track this coordinate frame with regards to the
world coordinate frame
which is initialised based on the gravity direction. Given that again, the
camera is pointing forwards and parallel to the ground, the Z axis will be
initialised opposite of the gravity direction. Then the X axis will point
towards the right side and Y will be pointing forwards.
So, if you start up the RealSense, parallel to the ground as in most cases and start moving it forwards, you will notice our Y estimate increasing. Similarly, if you move it upwards, then you’ll notice a positive increase in the Z coordinate.
Following is an image from RVIZ after having moved the camera slightly forwards.
In this image the world frame is called
slamcore/map frame, and the pose
estimation / IMU frame is called
slamcore/imu_0 frame. The
frame is discussed in the following section.
Many popular ROS frameworks, like the navigation stack abide to the ROS Coordinate Frames convention - REP-105, and thus requires two transformations to operate:
base_footprint (or sometimes called
base_link) is the main
frame of reference of the robot platform in use.
This way a ROS node that’s interested in the pose of the robot can query
base_footprint. If it queries the former, it will get the most
accurate estimation of the robot pose, however, that may include
discontinuities or jumps. On the other hand, the
base_footprint transform is
drift-y and overall less-accurate but is guaranteed to change smoothly
Traditionally the localisation module would compute the
and would use the latest
base_footprint, as published by the odometry node (e.g.
dead reckoning via wheel odometry) to eventually publish
odom and abide
To abide to this standard and also increase the overall accuracy of these
transforms, the SLAMcore ROS Wrappers incorporate the latest odometry
information and publish both the
base_footprint transforms. This way
we can provide a smooth
base_footprint transform that potentially uses the wheel-odometry,
as well as information from the visual and inertial sensors.