Process

State estimation

You might say: localization, where am I

State estimation combines a motion model with noisy sensor measurements to infer pose, velocity, and uncertainty.

What it does

Gives planners and controllers their best current belief about the robot.

Ask

How uncertain is the estimate, and does that uncertainty match observed errors?

What to look for

1. Pose, velocity and uncertainty estimate
2. Estimate checked against observed errors

Parts

• Bayes filter: A Bayes filter predicts a belief through a motion model and then conditions it on a new measurement.
• Odometry: Odometry estimates incremental motion by accumulating wheel, image, inertial, or scan-matching measurements.
• SLAM: SLAM estimates a robot trajectory and a map together from observations, so the map and pose constrain one another.

Sources

1. Timothy D. Barfoot, State Estimation for Robotics, Chs. 3, 6–8This classic technique for linear systems (whose measurements are corrupted by Gaussian noise) is famously known as the Kalman filter, and has been the workhorse of estimation for the more than 60 years since its inception.

Use it on your project

teardown-apply: hold "State estimation" (state-estimation) from the teardown at https://teardownatlas.com/robotics/ against my project

Paste it into a coding agent that has the teardown skills.

An AI agent wrote this map with the teardown skill. It can contain errors: check the sources before you rely on a part. About and legal