- Cyrill Stachniss
Localization - Estimating robot's pose
Mapping - Task of modelling the environment
Where am I --><-- What does the world look like
Full SLAM estimates the entire navigation path whereas, Online SLAM seeks to recover only the most recent pose
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State estimation - What is the state of the world? Typically, Sensor data required to build the map of the environment.
- Estimating Semantics - Understanding what we see
- Estimating Geometry - Understanding what the world looks like
Both can be fused.
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Action Selection - Which action should state execute? Typically, physical navigation
These influence each other. It's a chicken-and-egg problem.
Given
Wanted
We estimate the probability distribution
- Uncertainty both in robot motion and observations
- Use of probability theory to explicitly represent the uncertainty
- Robot path and map both are unknown
- Map and pose estimates are correlated
- Mapping between observations and the map is unknown
- issue of divergence - picking wrong data associations can have catastrophic consequences
- Kalman Filter
- Particle Filter
- Graph based representations (Focus on this) - Read a tutorial on Graph-based SLAM
- Introduction
- Bayes Filter
- Occupancy Grid Maps
- Motion Model
- Observation Models
- EKF Localization
- Monte Carlo Localization
- Iterative Closest Point
- Graph-based SLAM
- Visual Features and RANSAC
I took this course due to an upcoming project in my company. I attended every Lecture and finished each Assignment. I believe this is one of the best online courses on mobile robotics. Prof. Stachniss explains so well.
I have provided my assignments here, which I completed entirely independently during the last two weeks without any external help. I enjoyed the course, assignments in particular and as of May 2021, I know of no online solutions for MSR-2 assignments except mine. My answers are not efficient ones but certainly correct. If you clone/download it, find an efficient solution, don't forget to raise an issue or a pull request.
Thanks for passing by!