Table of Contents
Active Simultaneous Localization and Mapping (SLAM) involves selecting actions that maximize information gain to improve the robot’s understanding of its environment. Calculating information gain helps determine the most informative actions to take during the planning process.
Understanding Information Gain
Information gain measures the reduction in uncertainty about the environment or the robot’s position after acquiring new data. It is often quantified using entropy, which represents the uncertainty in a probability distribution.
Calculating Information Gain in Active SLAM
The calculation involves predicting the expected reduction in entropy resulting from potential actions. This process typically includes simulating sensor measurements and updating the belief state accordingly.
Steps for Calculation
- Predict possible sensor readings for each candidate action.
- Update the belief state based on these predicted measurements.
- Calculate the entropy before and after the update.
- Subtract the post-measurement entropy from the prior entropy to obtain the information gain.