C++ implementation of Lidar Obstacle Detection using Point Cloud Library

Overview

This project is part of the Udacity Sensor Fusion nanodegree program. It focuses on detecting obstacles using Lidar data, implementing segmentation, clustering, and tracking of objects. The repo has my own implementations of the Ransac and Clustering algorithms as well.

Project Structure

The project consists of several components including data handling, segmentation using the 3D RANSAC algorithm, and clustering using the Euclidean clustering algorithm with a KD-Tree.

Requirements

• Ubuntu 16.04
• PCL - v1.7.2
• C++ v11
• gcc v5.5

Local Installation

Ubuntu

1. Clone this github repo:

   git clone https://github.com/atharvahude/Lidar-Obstacle-Detection.git

2. Execute the following commands in a terminal

   cd ~/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.

Note The library version of PCL being distributed by the apt repository for 18.04 and 20.04 are both older than v1.11. The following links have the information regarding the versions-

Bionic 18.04 Focal 20.04

You can either build PCL from source (for v1.11) or use the older version.

PCL Build from Source

PCL Source Github

Video Demo

![Watch the video]