Autonomous Robots: Localization

Learn how to compute 2D distances from a location to multiple polls, then apply least-squares minimization to estimate the robot’s position from pole measurements.

Apply Bayes rule to a 15-location localization problem with three polls, computing priors, detections, and posteriors. Shift probabilities as the robot moves and prepare for particle filters.

Walk through a localization assignment, showing a robot class, Bayes' rule updates, shift priors, and polling detection in Python. See how posteriors evolve as polls are detected and robot moves.

Assignment two part two walkthrough explains incorporating movement uncertainty into Bayes rule localization, using a 90 percent and 10 percent move model and shifting priors to handle motion overshoot.

Explore movement uncertainty in autonomous robot localization by shifting from discrete to continuous positions, using a particle filter with normal motion noise, mean and sigma, and evaluating ten million predictions.

Develop a realistic measurement model for the simulator: a range-3 distance sensor reports the closest object distance or -100 if none, guiding the particle filter through practical scenarios.

Update particle weights in autonomous robot localization by accounting for measurement uncertainty with probability density functions, sigma, and confidence intervals to identify the most likely position.

Explore how to implement and test the probability density function to update particle weights using the distribution and measurement sigma.

delivers the part five resample particles solution, showing weight collection, resampling with choices, and creating new particles; highlights high-weight color coding and movement sigma effects on spread.

Explore the set three part six solution, showing how to resample particles when weights collapse and to spread particles uniformly to improve localization around the robot.

Explore the assignment four part four solution for autonomous robot localization, detailing the resample particles function, weights handling, and scale-based noise control yielding near-zero angle error and 1.1 distance error.

Explore a robotics localization solution using a particle filter, with fixed seeds for debugging, uniform particle distribution, and step-by-step updates—move, measure, predict, resample—highlighting convergence and particle-count effects.