Merge pull request #12 from bryant1410/master

Fix broken headings in Markdown files

README.md

@@ -1,22 +1,22 @@
 This is an OpenCV 3.0 based implementation of a monocular visual odometry algorithm.
 
-##Algorithm
+## Algorithm
 Uses Nister's Five Point Algorithm for Essential Matrix estimation, and FAST features, with a KLT tracker.
 More details are available [here as a report](http://avisingh599.github.io/assets/ugp2-report.pdf), and
 [here as a blog post](http://avisingh599.github.io/vision/monocular-vo/). 
 
 Note that this project is not yet capable of doing reliable relative scale estimation, 
 so the scale informaion is extracted from the KITTI dataset ground truth files.
 
-##Demo Video
+## Demo Video
 
 [![Demo video](http://share.gifyoutube.com/Ke1ope.gif)](http://www.youtube.com/watch?v=homos4vd_Zs)
 
 
-##Requirements
+## Requirements
 OpenCV 3.0
 
-##How to compile?
+## How to compile?
 Provided with this repo is a CMakeLists.txt file, which you can use to directly compile the code as follows:
 ```bash
 mkdir build
@@ -25,21 +25,21 @@ cmake ..
 make
 ```
 
-##How to run? 
+## How to run? 
 After compilation, in the build directly, type the following:
 ```bash
 ./vo
 ```
-##Before you run
+## Before you run
 In order to run this algorithm, you need to have either your own data, 
 or else the sequences from [KITTI's Visual Odometry Dataset](http://www.cvlibs.net/datasets/kitti/eval_odometry.php).
 In order to run this algorithm on your own data, you must modify the intrinsic calibration parameters in the code.
 
-##Performance
+## Performance
 ![Results on the KITTI VO Benchmark](http://avisingh599.github.io/images/visodo/2K.png)
 
-##Contact
+## Contact
 For any queries, contact: avisingh599@gmail.com
 
-##License
+## License
 MIT