Project 2: Localization

TAs: Miles, Kurt

Goal: Familiarity with probability concepts & Bayes nets, show how more information leads to less uncertainty


Implementation: The localization problem of robotics: using a map of the first floor of the CIT paired with associated LIDAR data, determine the current location.

Overview
Part I:
Implement a naive, stateless Kalman Filter localization function. Given a map of the first floor of the CIT and a set of LIDAR data points, determine the most likely position of the robotic agent. Assume all positions are equally likely a priori. The naive Kalman Filter should NOT keep track of previous positions, taking into account solely the LIDAR data given on a single step. On one hundred randomly started trials, what is the average maximum likelihood achieved? What is the average accuracy of the stateless Kalman Filter over these one hundred trials (how often is the most likely position the true position)?

Part 2:
Implement a stateful Kalman Filter localization function. (Ref: http://imgur.com/KrWq9NI). This function will be used by the step() function provided in the source code that will perform a 'step' of the agent in the CIT domain, providing a new set of LIDAR data. Your stateful Kalman Filter function should keep track of the LIDAR data yielded by previous calls to the step function. On ten randomly started trials, how many 'steps' does it take to reach a likelihood of 0.5 for a single position? 0.7? 0.9?. Assume all positions are equal likely a priori.

Part 3:
Implement a stateful Kalman Filter localization function that does not assume all positions are equally likely a priori. On ten randomly started trials, how many 'steps' does it take to reach a likelihood of 0.5 for a single position? 0.7? 0.9?.

Extra Credit:
- Multiple floors
- Particle filtering

Timeline:

Out: Tuesday 2/11

In: Thursday 2/27