# Tutorials

The tutorial chapter covers the following content up to now:

## Beginner Level

* [**Powering and Programming Interface**](https://cloudkerneltech.gitbook.io/kerlouduav/userguide/tutorial/powerprog): This tutorial walks you through how to setup the power connection and start programming with the Kerloud UAV series.
* [**Offboard Control with Mavros (C++)**](https://cloudkerneltech.gitbook.io/kerlouduav/userguide/tutorial/offboard): This tutorial introduces an offboard control example (C++ language) for a waypoint mission using the mavros package.
* [**Offboard Control with Mavros (Python)**](https://cloudkerneltech.gitbook.io/kerlouduav/userguide/tutorial/offboard_python): The tutorial introduces an example on how to operate Kerloud UAV with the mavros package via Python.
* [**Indoor Positioning with Optical Flow**](https://cloudkerneltech.gitbook.io/kerlouduav/userguide/tutorial/opticalflow_indoor): This tutorial presents details to realize indoor positioning based on optical flow with minimum setup for Kerloud UAV.
* [**Flight Data Analysis**](https://cloudkerneltech.gitbook.io/kerlouduav/userguide/tutorial/flight_log): This tutorial covers procedures to analyze flight performance with onboard data logs.

## Intermediate Level

* [**Virtual Simulation**](https://cloudkerneltech.gitbook.io/kerlouduav/userguide/tutorial/simulation): This tutorial provides steps to explore a virtual simulation environment for autonomous flights.
* [**Camera Pod Operation**](https://cloudkerneltech.gitbook.io/kerlouduav/userguide/tutorial/campod): This tutorial shows details on how to operate the camera pod equipped on a Kerloud 600 UAV.
* [**Real Time Visual Recognition**](https://cloudkerneltech.gitbook.io/kerlouduav/userguide/tutorial/visualrecog): This tutorial guides you through how to get the Nvidia Jetson computer started for real time visual recognition with the onboard CSI camera.
* [**Deep Learning in ROS**](https://cloudkerneltech.gitbook.io/kerlouduav/userguide/tutorial/ros_deeplearning): This tutorial introduces the details on how to integrate the deep learning capability from the Nvidia community in ROS.
* [**Enabling Autonomous Indoor Flight with a Tracking Camera**](https://cloudkerneltech.gitbook.io/kerlouduav/userguide/tutorial/vision_indoor): This tutorial describes the steps to achieve autonomous indoor flights with our Kerloud Nano VIO Indoor UAV.

## Advanced Level

* [**Hardware-in-the-loop Simulation in Airsim Environment**](https://cloudkerneltech.gitbook.io/kerlouduav/userguide/tutorial/airsim_hil_sim): This tutorial illustrates how to perform HIL (hardware-in-the-loop) simulation in Airsim environment with a Kerloud autopilot.
* [**Visual Inertial System (VINS) with Stereo Vision and GPU Acceleration**](https://cloudkerneltech.gitbook.io/kerlouduav/userguide/tutorial/vins): This tutorial provides a guidance to realize a GPU-accelerated visual inertial system (VINS) by fusing stereo vision and IMU data.
* [**DASA Swarm Simulation Toolbox**](https://cloudkerneltech.gitbook.io/kerlouduav/userguide/tutorial/swarm_sim): This tutorial introduces the DASA swarm simulation toolbox to facilitate the conceptualization and validation of UAV swarm algorithms.

### Coming Soon

More advanced features are coming soon for our Kerloud UAV series, and they are not limited to:

* SLAM (Simultaneous Localization and Mapping)
* Path planning
* Obstacle avoidance
* Aggressive fast flight …

So stay with us and stay tuned!