Basic Odometry and Localization

Recommended reading: ROS transform tutorials, ROS odometry tutorial, and ROS IMU documentation, ROS GPS documentation

One of the essential information that the robot must generate is its odometry – how the robot changed its position over time.

Two of the simplest ways to generate odometry is to use IMU (inertial measurement unit) and the GPS.

IMU’s measure accelerations of 6 degree – 3 linear accelerations (x,y,z) and 3 rotational acceleration (roll, pitch, yaw), using accelerometer, gyroscopes, and sometimes magnetometers (which calculates the acceleration based on its interactions with Earth’s magnetic field).

One of the drawbacks of IMU is that of most of the sensors – if you solely use IMU for the odometry, the odometry will be off more and more so as the time goes by and errors from the sensors accumulate.

One way to prevent excessive accumulation of misreadings is to “calibrate” the readings against the data from other sensors, in particular,  that of sensors that can get independent reading each time (e.g. GPS/Compass).

The magnetometers serve the same role as the accelerometers and gyroscopes, but its addition serves as a calibrator for the readings from other two sensors. However, its use also means that one needs to make sure that the IMU’s are not next to any other significant magnetic field other than that of earth, such as that of which can be generated by power-hungry electronics.

See this Wikipedia page on IMU:

GPS provides the device with the global position, and is often used as the ultimate calibration data against all the sensors. And with the GPS position data over time, it can likewise be used to generate odometry.

However, due to the nature of GPS, solely using GPS for odometry is not recommended. For one, since GPS receives data from the satellites, the position data is received with a long latency compared to other sensors, leading to inaccurate odometry. Also, GPS require open space to be able to communicate to the satellites and fails to get any data if space is not provided. Lastly, most GPS are not accurate and could have error upto 1 meter or more.

Despite these problems of each sensors, IMU and GPS can be used well together to generate decent odometry – see Uber/Google Maps.

However, in order to do so, two things must happen. GPS and IMU data must be combined

GPS and IMU data must be combined together appropriate to form one, more accurate odometry data. This is done in ROS with a package called robot_pose_ekf, which uses something called efficient Kalman filter to combine multiple sensor data together.

Second, GPS and IMU’s data needs to be provided relative to the robot, not the sensors. While this may not be necessary when the robot and the sensors are small enough and situated “correctly” to each other (sensor is not too far from robot, etc), this will become an issue as the robot gets larger and sensors more distant from each other.

This problem is solved using the tf package in ROS, which provides the transformation between the sensors and the robot. In tf package, robot is often labeled to be the “base_link” with which all the sensors are located relative to it – as specified by the transformation, or the specified distance between the robot and the sensor. Without that transform information, the combination of the odometry data will not be accurate, as sensors could provide different information based on their location relative to the robot.

So, to specify explicitly, this is what needs to be done:

  1. Get the sensor data from the IMU and the GPS.
  2. Transform both IMU and GPS relative to the robot.
  3. Combine IMU and the GPS data using EKF.

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