Landmark Detection & Robot Tracking is the third project in Udacity's Computer Vision Nanodegree. In this project, I implemented SLAM (Simultaneous Localization and Mapping) for a 2 dimensional world, creating a map of an environment from only sensor and motion data gathered by a robot, over time. SLAM gives us a way to track the location of a robot in the world in real-time and identify the locations of landmarks such as buildings, trees, rocks, and other world features. This is an active area of research in the fields of robotics and autonomous systems.
The project is broken up into three Python notebooks; the first two are for exploration of provided code, and a review of SLAM architectures.
Notebook 1 : Robot Moving and Sensing
Notebook 2 : Omega and Xi, Constraints
Notebook 3 : Landmark Detection and Tracking