LidarDetection
Implementation of euclidean clustering for lidar sensors
Welcome to the Sensor Fusion course for self-driving cars.
In this course we will be talking about sensor fusion, whch is the process of taking data from multiple sensors and combining it to give us a better understanding of the world around us. we will mostly be focusing on two sensors, lidar, and radar. By the end we will be fusing the data from these two sensors to track multiple cars on the road, estimating their positions and speed.
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.
Installation
Ubuntu
$> sudo apt install libpcl-dev
$> cd ~
$> mkdir build && cd build
$> cmake ..
$> make
$> ./environment
Windows
http://www.pointclouds.org/downloads/windows.html
MAC
Install via Homebrew
- install homebrew
- update homebrew
$> brew update
- add homebrew science tap
$> brew tap brewsci/science
- view pcl install options
$> brew options pcl
- install PCL
$> brew install pcl
Prebuilt Binaries via Universal Installer
http://www.pointclouds.org/downloads/macosx.html
NOTE: very old version