Within the navigation project, there are 2 major transformations that need to be provided, according to community standards. The map to odom transform is provided by a positioning system (localization, mapping, SLAM) and odom to base_link by an odometry system.
REP 105 defines the frames and conventions required for navigation and the larger ROS ecosystem. These conventions should be followed at all times to make use of the rich positioning, odometry, and SLAM projects available in the community.
In a nutshell, REP-105 says that you must, at minimum, build a TF tree that contains a full map → odom → base_link → [sensor_frames] for your robot. TF2 is the time-variant transformation library in ROS 2 we use to represent and obtain time-synchronized transformations. It is the job of the global positioning system (GPS, SLAM, Motion Capture) to, at minimum, provide the map → odom transformation. It is then the role of the odometry system to provide the odom → base_link transformation. The remainder of the transformations relative to base_link should be static and defined in your URDF.
It is the job of the global positioning system (GPS, SLAM, Motion Capture) to, at minimum, provide the map → odom transformation. Nav2 provides amcl which is an Adaptive Monte-Carlo Localization technique based on a particle filter for localization in a static map. Nav2 also provides the SLAM toolbox as the default SLAM algorithm for use to position and generate a static map.
These methods may also produce other output including position topics, maps, or other metadata, but they must provide that transformation to be valid. Multiple positioning methods can be fused together using robot localization, discussed more below.
It is the role of the odometry system to provide the odom -> base_link transformation. Odometry can come from many sources including LIDAR, RADAR, wheel encoders, VIO, and IMUs. The goal of the odometry is to provide a smooth and continuous local frame based on robot motion. The global positioning system will update the transformation relative to the global frame to account for the odometric drift.
Robot Localization is typically used for this fusion. It will take in N sensors of various types and provide a continuous and smooth odometry to TF and to a topic. A typical mobile robotics setup may have odometry from wheel encoders, IMUs, and vision fused in this manner.
The smooth output can be used then for dead-reckoning for precise motion and updating the position of the robot accurately between global position updates.
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