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mobile_sensing_robotics

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Mobile Sensing and Robotics Course - 2020/21 By Prof. C. Stachniss

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# Mobile Sensing and Robotics - 2020/21

## Instructor
* Cyrill Stachniss

## Resources
* [MSR Part 1 Videos](https://www.youtube.com/watch?v=5KZpWAe9hSk&list=PLgnQpQtFTOGQEn33QDVGJpiZLi-SlL7vA)
* [MSR Part 2 Videos](https://www.youtube.com/watch?v=mQvKhmWagB4&list=PLgnQpQtFTOGQh_J16IMwDlji18SWQ2PZ6)
* [MSR Part 2 Slides](https://www.ipb.uni-bonn.de/html/teaching/msr2-2021/msr2-2021-slides-pdf.zip)

## SLAM - Simultaneous Localization and Mapping

**Localization** - Estimating robot's pose

**Mapping** - Task of modelling the environment

![SLAM Example](https://www.societyofrobots.com/images/sensors_IRSLAM.gif)

*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*

## Two fundamental questions in Mobile Robotics

1. **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.

2. **Action Selection** - Which action should state execute? Typically, *physical navigation*

These influence each other. It's a chicken-and-egg problem.

## Mathematical description of the problem
**Given**
1. The robot's controls ![u_{1:T}](https://render.githubusercontent.com/render/math?math=%5Clarge+%5Cdisplaystyle+u_%7B1%3AT%7D%0A)
2. Sensor Observations ![z_{1:T}](https://render.githubusercontent.com/render/math?math=%5Clarge+%5Cdisplaystyle+z_%7B1%3AT%7D%0A)

**Wanted**
1. Map of the environment *m*
2. Path of the robot ![x_{0:T}](https://render.githubusercontent.com/render/math?math=%5Clarge+%5Cdisplaystyle+x_%7B0%3AT%7D%0A)

**We estimate the probability distribution** ![\begin{align*} p(x_{0:T}, m \, | \, z_{1:T}, u_{1:T}) \end{align*}](https://render.githubusercontent.com/render/math?math=%5Clarge+%5Cdisplaystyle+%5Cbegin%7Balign%2A%7D%0Ap%28x_%7B0%3AT%7D%2C+m+%5C%2C+%7C+%5C%2C+z_%7B1%3AT%7D%2C+u_%7B1%3AT%7D%29%0A%5Cend%7Balign%2A%7D%0A)

### Solved using probabilistic approaches because
- Uncertainty both in robot motion and observations
- Use of probability theory to explicitly represent the uncertainty

## Why is SLAM a Hard problem to solve?
1. Robot path and map both are unknown
2. Map and pose estimates are correlated
3. Mapping between observations and the map is unknown
4. issue of divergence - picking wrong data associations can have catastrophic consequences

## Paradigms for solving SLAM problems
* Kalman Filter
* Particle Filter
* Graph based representations *(Focus on this)* - [Read a tutorial on Graph-based SLAM](https://github.com/ZohebAbai/mobile_sensing_robotics/blob/main/A%20Tutorial%20on%20Graph-Based%20SLAM.pdf)

## Notebooks (Exercises with Notes)
* [Introduction](https://github.com/ZohebAbai/mobile_sensing_robotics/blob/main/Introduction.ipynb)
* [Bayes Filter](https://github.com/ZohebAbai/mobile_sensing_robotics/blob/main/Bayes_Filter.ipynb)
* [Occupancy Grid Maps](https://github.com/ZohebAbai/mobile_sensing_robotics/blob/main/Occupancy_Grid_Maps.ipynb)
* [Motion Model](https://github.com/ZohebAbai/mobile_sensing_robotics/blob/main/Motion_Models.ipynb)
* [Observation Models](https://github.com/ZohebAbai/mobile_sensing_robotics/blob/main/Observation_Models.ipynb)
* [EKF Localization](https://github.com/ZohebAbai/mobile_sensing_robotics/blob/main/EKF_Localization.ipynb)
* [Monte Carlo Localization](https://github.com/ZohebAbai/mobile_sensing_robotics/blob/main/Monte_Carlo_Localization.ipynb)
* [Iterative Closest Point](https://github.com/ZohebAbai/mobile_sensing_robotics/blob/main/Iterative_Closest_Point.ipynb)
* [Graph-based SLAM](https://github.com/ZohebAbai/mobile_sensing_robotics/blob/main/Graph_based_SLAM.ipynb)
* [Visual Features and RANSAC](https://github.com/ZohebAbai/mobile_sensing_robotics/blob/main/Visual_Features_RANSAC.ipynb)

## Opinion/Comments
> 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!*

mobile_sensing_robotics

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