Robotics
Visual-inertial odometry
Definition
Visual-inertial odometry estimates a moving system's motion by combining camera observations with inertial measurements. It typically estimates position, orientation, velocity, and sensor biases over time.
Also known as: VIO
Updated
Cameras and inertial sensors contribute different information
Images constrain motion through visible scene features. An inertial measurement unit supplies rapid measurements of angular velocity and specific force. Forster et al. explain how these complementary inputs can support motion estimation and recover metric scale that a monocular camera alone cannot directly observe.
A head-mounted camera and IMU can, for example, help estimate a humanoid's motion through a room. This estimate still needs consistent sensor frames and calibration.
Preintegration keeps the problem manageable
Inertial readings often arrive more frequently than camera frames. The cited method combines many readings between selected image frames into relative-motion constraints. Its estimator also accounts for IMU bias, rather than treating every acceleration measurement as exact.
Local motion is not a permanent global reference
VIO is a form of odometry. Without additional global constraints, accumulated error can grow. ROS frame conventions distinguish a continuous local odometry frame from a globally corrected map frame. Adding place recognition and loop closure changes the larger system into a visual-inertial SLAM pipeline.
Sources
Related terms
Inertial measurement unit
An inertial measurement unit is a sensor assembly that typically combines accelerometers and gyroscopes to measure specific force and angular velocity. Some devices also provide magnetometer readings or estimated orientation.
Odometry
Odometry estimates changes in a robot's position and orientation from motion measurements over time. Its accumulated pose provides a local reference that can drift as measurement errors build up.
Sensor fusion
Sensor fusion combines information from multiple sensors or estimation sources to produce a shared estimate. The combination must account for coordinate frames, timing, uncertainty, and dependence between inputs.