Fixed README file

README.md

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-LSD-slam for ios
+# MobileSLAM
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+Acknowledgements:  This repository contains code from https://github.com/tum-vision/lsd_slam which we are modifying for a term project.  Many thanks go out to the TUM Vision Group. The methodogy for this method, LSD-SLAM, can be found in the following paper:
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+LSD-SLAM: Large-Scale Direct Monocular SLAM, J. Engel, T. Schöps, D. Cremers, ECCV '14
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+Title: Direct Visual Odometry on Mobile Device
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+Team: Jennifer Lake & Guanhang Wu
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+Summary: For this project, we want to implement direct method visual odemetry algorithm for ios device. Specifically, estimating a Semi\-Dense Depth Map of the scene and reconstructure a 3D Mesh.
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+Background: Our project will utilize the computation power of the modern SOC on the mobile device. Direct methods are known for being computationally intensive. Paper[1] discribed a way to estimate semi-dense map, which reduces the computation dramatically. As the computation power grows, the visual odometry algorithm could be running in real-time in a modern mobile device. 
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+The Challenge: There is no direct method implemented for ios device at this moment. We need to implement the tracking, depth map estimation algorithm by ourselves. In addition, real-time visual odometry needs to be modified to run in a multi-threaded environment.
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+Goals & Deliverables: The goal for this project is to implement the direct visual odometry algorithm on the ios device.
+We will try to make the algorithm running in the real-time. If that is not permissible in the time allowed, we will explore where the algorithm creates bottlenecks and try to mitigate some of these.  By the end of this project, we should be able to show which parts of the algorithm are creating bottlenecks, show some performance improvement, and be able to explain how in theory we could make this algorithm realtime. Futhermore if there is time, we want to add virtual object on the detected 3D surface.
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+Schedule: 
+Week 1 (November 6th- 12th): Port existing C++ code in xcode project
+Week 2 (November 13th - 20th): Performance test existing code, identify bottlenecks and possible solutions (Project checkpoint due November 16th)
+Week 3 (November 21st - 27th): Implement optimizations
+Week 4 (Novemeber 28th - December 3rd): Performance test optimized code, identify further refinements, and bottlenecks
+Week 5 (December 4th - December 10th): Create Presentation and Write Report (Presentation due December 8th, Report due December 11th)
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+[1] T. Schöps, J. Engel, D. Cremers, Semi-Dense Visual Odometry for AR on a Smartphone, In International Symposium on Mixed and Augmented Reality, 2014.