Lidar Obstacle Detection

Project for Udacity's Sensor Fusion Engineer Nanodegree Program

Project Goals

• Implement Obstacle detection pipeline on PCD from a lidar sensor
• use pcl-library for general data handling and initial testing
• implement following steps:
  • PCD filtering, for reducing computational cost, without loss of detail
  • Segment the filtered cloud into two parts, road plane and obstacles, using RANSAC based 3D-plane extraction
  • Cluster the obstacle cloud, using K-D Tree for 3D space and Eucledian clustering
  • Find bounding boxes for the clusters

Dependencies:

The configuration I used

• Ubuntu 16.04 OS
• cmake >= 3.14
• gcc/g++ >= 8.0
• PCL >= 1.2 : The code extensively utilizes the Point Cloud Library (PCL).

Notes on some files & folders

• README.md: this file.
• ./src/
  • environment.cpp - main function , city Block function contains the Lidar obstacle detection pipeline.
  • kdtree.h - KD Tree structure definition for Eucledian clustering of Point cloud.
  • processPointClouds.cpp & processPointClouds.h - functions for point-cloud processing.
  • /render/... - contains functions for rendering point clouds and bounding boxes
  • /sensors/.. - contains point-cloud-data files and functions for use with synthetic data.

Build and Run

clone this repository, enter the cloned directory/folder and build:

cd SFND_Lidar_Obstacle_Detection
mkdir build && cd build
cmake ..
make

to run, use following from within the build folder:

./environment

Lidar-Obstacle-Detection

once ./environment is launched pcd data is read from files at /sensors/data/pcd/data_1/ and point cloud is processed. Input Lidar Stream And Object Detections are rendered in viewers as shown below:

Input Lidar Stream

Bounding Box Object Detections

Resources

• To install PCL, C++ https://larrylisky.com/2014/03/03/installing-pcl-on-ubuntu/