This is the first project in the Udacity Sensor Fusion Nanodegree. It consists of solving the following problem:

• Pointcloud processing with the help of PLC functions
• Understanding and deploying an implementation of RANSAC for ground segmentation
• Understanding and deploying the KD tree structure for the purpose of Euclidean distance clustering for the identification of key elements on the road

1. Clone this github repo:

   cd ~
   git clone https://github.com/udacity/SFND_Lidar_Obstacle_Detection.git

2. Edit CMakeLists.txt as follows:

cmake_minimum_required(VERSION 2.8 FATAL_ERROR)

add_definitions(-std=c++14)

set(CXX_FLAGS "-Wall")
set(CMAKE_CXX_FLAGS, "${CXX_FLAGS}")

project(playback)

find_package(PCL 1.11 REQUIRED)

include_directories(${PCL_INCLUDE_DIRS})
link_directories(${PCL_LIBRARY_DIRS})
add_definitions(${PCL_DEFINITIONS})
list(REMOVE_ITEM PCL_LIBRARIES "vtkproj4")


add_executable (environment src/environment.cpp src/render/render.cpp src/processPointClouds.cpp)
target_link_libraries (environment ${PCL_LIBRARIES})

3. Execute the following commands in a terminal Note The [CMakeLists.txt] file provided in this repo can be used locally if you have the same package versions as mentioned above. If you want to run this project locally (outside the Udacity workspace), please follow the steps under the Local Installation section. Note The [CMakeLists.txt] file provided in this repo can be used locally if you have the same package versions as mentioned above. If you want to run this project locally (outside the Udacity workspace), please follow the steps under the Local Installation section. Note The [CMakeLists.txt] file provided in this repo can be used locally if you have the same package versions as mentioned above. If you want to run this project locally (outside the Udacity workspace), please follow the steps under the Local Installation section. Note The [CMakeLists.txt] file provided in this repo can be used locally if you have the same package versions as mentioned above. If you want to run this project locally (outside the Udacity workspace), please follow the steps under the Local Installation section.

   sudo apt install libpcl-dev
   cd ~/SFND_Lidar_Obstacle_Detection
   mkdir build && cd build
   cmake ..
   make
   ./environment

   This should install the latest version of PCL. You should be able to do all the classroom exercises and project with this setup.

1. install homebrew

2. update homebrew

   $> brew update

3. add homebrew science tap

   $> brew tap brewsci/science

4. view pcl install options

   $> brew options pcl

5. install PCL

   $> brew install pcl

6. Clone this github repo

   cd ~
   git clone https://github.com/udacity/SFND_Lidar_Obstacle_Detection.git

7. Edit the CMakeLists.txt file as shown in Step 2 of Ubuntu installation instructions above.

8. Execute the following commands in a terminal

   cd ~/SFND_Lidar_Obstacle_Detection
   mkdir build && cd build
   cmake ..
   make
   ./environment

1. Follow the steps here to install PCL.

2. Clone this github repo

   cd ~
   git clone https://github.com/udacity/SFND_Lidar_Obstacle_Detection.git

3. Edit the CMakeLists.txt file as shown in Step 2 of Ubuntu installation instructions above.

4. Execute the following commands in Powershell or Terminal

   cd ~/SFND_Lidar_Obstacle_Detection
   mkdir build && cd build
   cmake ..
   make
   ./environment

PCL Source Github

PCL Mac Compilation Docs