/SFND_Lidar_Obstacle_Detection

Added custom code for RANSAC road plane segmentation and euclidian clustering with KD-tree

Primary LanguageC++

Lidar obstacle detection with PCL

Lidar sensing gives us high resolution data by sending out thousands of laser signals. These lasers bounce off objects, returning to the sensor where we can then determine how far away objects are by timing how long it takes for the signal to return. Also we can tell a little bit about the object that was hit by measuring the intesity of the returned signal. Each laser ray is in the infrared spectrum, and is sent out at many different angles, usually in a 360 degree range. While lidar sensors gives us very high accurate models for the world around us in 3D, they are currently very expensive, upwards of $60,000 for a standard unit.

Radar data is typically very sparse and in a limited range, however it can directly tell us how fast an object is moving in a certain direction. This ability makes radars a very pratical sensor for doing things like cruise control where its important to know how fast the car infront of you is traveling. Radar sensors are also very affordable and common now of days in newer cars.

Sensor Fusion by combing lidar's high resoultion imaging with radar's ability to measure velocity of objects we can get a better understanding of the sorrounding environment than we could using one of the sensors alone.

This repo contains:

  • custom implementation of the ransac algorithm for road plane segmentation and kd-tree for euclidian clustering of obstacles;
  • code for Lidar simlutation;
  • code for real lidar point cloud streaming and viewing;

PCL Installation

Ubuntu

$> sudo apt install libpcl-dev
$> cd ~
$> git clone https://github.com/udacity/SFND_Lidar_Obstacle_Detection.git
$> cd SFND_Lidar_Obstacle_Detection
$> mkdir build && cd build
$> cmake ..
$> make
$> ./environment

Hint to Ubuntu 16.04 users: in case of errors during building - install libproj-dev & libopenni2-dev.

Windows

http://www.pointclouds.org/downloads/windows.html

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

Prebuilt Binaries via Universal Installer

http://www.pointclouds.org/downloads/macosx.html
NOTE: very old version

Build from Source

PCL Source Github

PCL Mac Compilation Docs

To do:

  • additional challenge: detect/track a bicyclist riding in front of the car, along with detecting/tracking the other surrounding obstacles in the scene, using the data at: src/sensors/data/pcd/data_2;