本文希望能發展出能夠在未知室內環境進行自動飛行的改裝過的Parrot AR. Drone 2.0四旋翼。目標希望能同時進行環境建模與自我定位，同時這些行為希望能只用搭載在載具上的控制器、傳感器，與單板電腦完成。
AR. Drone能夠在室內進行穩定而且安全的試飛，但是也因此載重能力不佳。因此在室內定位方式的選擇上，使用了只需要一個普通攝影機的單視角同步定位與地圖建構法。又為了能夠得到可以用來避開障礙的3D地圖，從眾多單視角同步定位與地圖建構法中選擇了LSD-SLAM。
單視角同步定位與地圖建構法無法估計出絕對的姿態與位置，為了解決這個問題，需要把超音波感測器的高度值當做改正信號，使用遞迴最小平方法來求出絕對的姿態與位置。
另外這個系統的軟體架構由ROS來統合，並且以有名的機器人路徑規劃軟體，Moveit，來完成自動產生路徑並且自動飛行的功能。
由於時間不足，最終完成了軟體架構與遞迴最小平方法的測試，但是沒有進行完全自動飛行。由於基本功能都以經驗正過了，這個研究方向值得繼續完成。

In this thesis, we try to develop a system capable of navigating a modified Parrot AR. Drone 2.0 in an indoor unknown environment using only onboard hardware like sensors, controller and single board computer. The quadcopter should be able to accomplish three tasks:
1. Constructing the 3D map of the surrounding and determining the position of itself in that environment.
2. Three-dimensional position control and following a given path consisting of way points.
3. Planning feasible path between user-specified points and avoiding the obstacles using the 3D map we construct.
AR. Drone is very safe and lightweight but also has very limited payload, so the particular challenge is dealing with the limited sensor variety and quality available by designing an effective data fusion framework.
To achieve the goal of positioning and mapping, we decide to use normal webcam and perform monocular SLAM to lower the CPU loading because single board computer only has limited computing power. Furthermore, we choose the LSD SLAM because it can reconstruct the 3D environment. Although the depth map is semi-dense only, but this is a good trade-off between map quality and CPU load: If you choose other keypoint-based monocular SLAMs for their light CPU loading, the map consisting of keypoints will not dense enough for obstacle avoiding. But even laptop cannot do a dense 3D reconstruction fast enough for online usage using common algorithm.
Monocular visual SLAM cannot navigate a quadcopter all by itself. That is, it can’t determine an absolute attitude w.r.t. any local coordinate system like NED or ENU frame, and also the distance it get has to be multiplied by an unknown scaling factor. The PX4 firmware has a reliable complementary-filter-based AHRS so we can estimate an attitude offset by comparing the attitude from PX4 AHRS to the one from LSD SLAM. For the estimation of the unknown scaling factor, we need the height reading from sonar sensor and use it to perform a slightly modified recursive least-squares method. Sonar doesn’t drift like barometer and the noise is relatively low.
Finally, for the path planning part, we use ROS inside our single board computer and choose Moveit, a very powerful and flexible planning package only available in ROS, as our solution.
The results of the software framework and the scale estimation are shown. Although the fully autonomous flight didn’t work because a lack of time, this thesis has proved the concept and the development of the system proposed here should be carry on.

[1] 2pcs lot 2 million pixels 1080P 30fps camera module+ free shipping. AliExpress. [Online] [Cited: 8 31, 2015.] http://www.aliexpress.com/item/2pcs-lot-2-million-pixels-1080P-30fps-camera-module-free-shipping/32233508943.html.
[2] AR.Drone 2.0 Quadricopter, Elite Edition, Sand. horizonhobby.com. [Online] [Cited: 8 31, 2015.] http://www.horizonhobby.com/ProductDisplay?product_identifier_token=product&urlRequestType=Base&productId=606885&catalogId=10001&categoryId=11443&errorViewName=ProductDisplayErrorView&urlLangId=-1&langId=-1&top_category=11010&parent_category_rn=11081.
[3] Bohren, Jonathan, Jay, Graylin Trevor and Crick, Christopher. gscam. ROS Wiki. [Online] [Cited: 08 30, 2015.] http://wiki.ros.org/gscam.
[4] Boyd, Stephen. Lecture 6 Least-squares applications. EE263: Introduction to Linear Dynamical Systems. [Online] [Cited: 8 31, 2015.] http://stanford.edu/class/ee263/lectures/ls-app.pdf.
[5] Crius FTDI Basic Breakout Arduino USB-TTL 6 PIN 3.3 5V for MWC MultiWii SE Lite. [Online] Shenzhen Dewang Model Ltd. [Cited: 8 31, 2015.] http://dprinterpens.buy.lightneasy.org/pz66f2493-crius-ftdi-basic-breakout-arduino-usb-ttl-6-pin-3-3-5v-for-mwc-multiwii-se-lite.html.
[6] Engel, J. Autonomous camera-based navigation of a quadrocopter. 2011. Master’s thesis, Technical University Munich.
[7] EP-N8508GS. [Online] EDUP. [Cited: 8 31, 2015.] http://www.szedup.com/show.aspx?id=1680.
[8] Ermakov, Vladimir. mavros. ROS Wiki. [Online] [Cited: 8 31, 2015.] http://wiki.ros.org/mavros.
[9] Faessler, Matthias, et al. 2015. Autonomous, Vision-based Flight and Live Dense 3D Mapping with a Quadrotor Micro Aerial Vehicle. Journal of Field Robotics. 2015.
[10] Hornung, A., et al. 2013. OctoMap: An Efficient Probabilistic 3D Mapping Framework Based on Octrees. Autonomous Robots. 2013.
[11] LSD-SLAM: Large-Scale Direct Monocular SLAM. Engel, Jakob, Schops, Thomas and Cremers, Daniel. 2014. 2014. European Conference on Computer Vision.
[12] Mihelich, Patrick, Konolige, Kurt and Leibs, Jeremy. image_proc. ROS Wiki. [Online] [Cited: 08 31, 2015.] http://wiki.ros.org/image_proc.
[13] Open Source Robotics Foundation. About ROS. ROS. [Online] [Cited: 08 30, 2015.] http://www.ros.org/about-ros/.
[14] PX4: A Node-Based Multithreaded Open Source Robotics Framework for Deeply Embedded Platforms. Meier, Lorenz, Honegger, Dominik and Pollefeys, Marc. 2015. 2015. ICRA.
[15] Sucan, Ioan A. and Chitta, Sachin. MoveIt! [Online] [Cited: 08 30, 2015.] http://moveit.ros.org/.
[16] tum-vision. lsd_slam. GithHub. [Online] [Cited: 08 30, 2015.] https://github.com/tum-vision/lsd_slam.
[17] Weiss, Stephan M. Vision based navigation for micro helicopters. 2012. PhD Thesis. University of Pennsylvania.
[18] XU3. ODROID. [Online] Hardkernel. [Cited: 08 31, 2015.] http://www.hardkernel.com/main/products/prdt_info.php?g_code=G140448267127.