GabrielOduori/SFND_Camera_LiDAR_Fusion

This repo contains all exercises and corresponding solutions for Sensor Fusion ND.

Sensor Fusion NanoDegree- Camera

Camera is the second course in the Sensor Fusion ND. The purpose of this repo is to provide the exercise code to the students, so that they can practice in local system.

This repo contains lesson-wise exercises and corresponding solutions for Udacity's Sensor Fusion ND.

A. List of Lesson-wise Exercises

1. Lesson 2: Autonomous Vehicles and Computer Vision
   • The OpenCV Library
2. Lesson 3: Engineering a Collision Detection System
   • Estimating TTC with Camera
   • Estimating TTC with Lidar
3. Lesson 4: Tracking Image Features
   • Descriptor Matching
   • Gradient-based vs. Binary Descriptors
   • Haris Corner Detection
   • Intensity Gradient and Filtering
   • Overview of Popular Keypoint Detectors
4. Lesson 5: Starter code for "Project: Camera Based 2D Feature Tracking" is available here - https://github.com/udacity/SFND_2D_Feature_Tracking
5. Lesson 6: Combining Camera and Lidar
   • Creating 3D-Objects
   • Lidar-to-Camera Point Projection
   • Object Detection with YOLO
6. Lesson 7: Starter code for "Project: Track an Object in 3D Space" is available here - https://github.com/udacity/SFND_3D_Object_Tracking

B. Dependencies for Running Locally

1. cmake >= 2.8

   • All OSes: click here for installation instructions

2. make >= 4.1 (Linux, Mac), 3.81 (Windows)

   • Linux: make is installed by default on most Linux distros
   • Mac: install Xcode command line tools to get make
   • Windows: Click here for installation instructions

3. OpenCV >= 4.1

   • This must be compiled from source using the -D OPENCV_ENABLE_NONFREE=ON cmake flag for testing the SIFT and SURF detectors.
   • The OpenCV 4.1.0 source code can be found here

4. gcc/g++ >= 5.4

   • Linux: gcc / g++ is installed by default on most Linux distros
   • Mac: same deal as make - install Xcode command line tools
   • Windows: recommend using MinGW

C. Build Instructions

1. Fork this repo to your Github account
2. Clone your Github repo.
3. Go to the top level directory for an exercise, and run the following commands on your terminal:

mkdir build && cd build
cmake ..
make
./<Executable_File_Name>

4. Update back the remote (online) repo so that you can use the updated code in the classroom workspace.

D. Pushing Large Files (>100MB) [Optional]

The file Combining Camera and Lidar/Object Detection with YOLO/detect_objects/dat/yolo/yolov3.weights is 236.52 MB; this exceeds GitHub's file size limit of 100.00 MB

Github blocks the files that have size>100MB, while pushing them. To push large files, Git provides an option called Git Large File Storage (LFS). See the instructions at https://git-lfs.github.com/ to use Git LFS. See http://git.io/iEPt8g for more information.

You can push large file only to unprotected remote branches. Master branch is by default protected. Read more here. Therefore, you'll have to push to a new unprotected branch, and later, merge it with the remote master. Use the commands below:

• Create a new local branch

git checkout -b <local_branch_name>

• Write the solution to your exercise.

• Install Git Large File Storage (LFS). For MacOS, use

brew install git-lfs
git lfs install
git lfs track "<Large_file_name_if_any>"
git add .gitattributes
git add <path_to_the_Large_file_if_any>
git config --global lfs.contenttype 0

• Add the modified files to the index area, and commit the changes

git add . --all   
git commit -m "your comment"

• To push the current local branch and set the remote as upstream:

git push --set-upstream origin <local_branch_name>

• Next, create a PR and merge the new branch with the remote master.