Lidar Obstacle Detection

This repo implements RANSAC for plane segmentation and Euclidean Clustering algorithm using KdTrees to perform clustering for obstacle detection on real lidar data.

Local Installation

Ubuntu

1. Clone this github repo:

   cd ~
   git clone https://github.com/farzingkh/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

   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.

MAC

Install via Homebrew

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/farzingkh/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