[MOOC] Apollo Lesson 3: Localization

Jan 27, 2021 Computer Vision Apollo
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This is my note for lesson 3 of MOOC course: Self-Driving Fundamentals - Featuring Apollo. Content: How the vehicle localizes itself with a single-digit-centimeter-level accuracy.

1 Localization methods in Apollo

  • The RTK (Real Time Kinematic) based method which incorporates GPS and IMU (Inertial Measurement Unit) information.
  • The multi-sensor fusion method which incorporates GPS, IMU, and LiDAR information.

2 Inertial navigation

Global Navigation Satellite System (GNSS) refers to a constellation of satellites providing signals from space that transmit positioning and timing data to GNSS receivers. The receivers then use this data to determine location. Global Positioning System (GPS) is a kind of GNSS.

Properties:

  • Accurate with RTK
  • Poor performance in urban area and canyons
  • Low frequency update (~10Hz) ➝ Too slow for realtime positioning on SDC.

Inertial Measurement Unit (IMU)

On Wikipedia: An inertial measurement unit (IMU) is an electronic device that measures and reports a body's specific force, angular rate, and sometimes the orientation of the body, using a combination of accelerometers, gyroscopes, and sometimes magnetometers.

Components of IMU:

  • Accelerometer: measures velocity and acceleration
  • Gyroscope: measures rotation and rotational rate
  • Magnetometer: establishes cardinal direction (directional heading)

Disadvantage: The IMU's motion error increase with time.

GPS + IMU

We can combine GPS + IMU to localize the car. On one hand, IMU compensates for the low update frequency of GPS. On the other hand, GPS corrects the IMU's motion errors.

3 LiDAR Localization

With LiDAR, we can localize a car by means of point cloud matching. This method continuously matches the detected data from LiDAR sensors with the preexisting HD map. ➝ Require constantly updated HD map ➝ Very difficult.

4 Visual localization

Can we use images from cameras to localize the car?

Yes, but using only camera is hard. We often combine images with other sensor signals.

Particle Filter: We use particles or points on the map to estimate our most likely location.

5 Apollo Localization

Apollo localization using input from multiple sources and use Kalman Filter for sensor fusion.

Sensor fusion for localization

Sensor fusion for localization

6 Kidnapped Vehicle

TODO: Try Kidnapped Vehicle Project.