jacobsayono / robot-localization

2D histogram filter for estimating uncertainty from moving and sensing

Robot Localization

2D histogram filter for estimating uncertainty from moving and sensing

main.ipynb

• Visualizes the probability of the robot's local position (shown by the size of the blue circle).

localizer.py

• Key functions in this histogram filter for localization.

• initialize_beliefs() - converts a 2D char vector to a 2D float vector containing initial probabilities

• sense() - the robot senses the color of the current grid point and calculates the resulting beliefs

• move() - move the robot by (x,y) and calculate the new beliefs

simulate.py

• A Simulation class made up of functions to simulate and test the localization algorithm visually.

helpers.py

• Other functions used to optimize localization.

• blur() - blurring parameter controls how much of a belief spills out into adjacent cells.

• normalize() - computes the correspond normalized version of that grid

• is_robot_localized() - robot has a strong opinion when the size of it's best belief is greater than twice the size of its second best belief