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Object Detection pipeline implemented using the Voxel Grid and ROI based filtering, 3D RANSAC segmentation, Euclidean clustering based on KD-Tree, and bounding boxes, by processing Point Cloud data from LiDAR sensor.

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ayushgoel24/Obstacle-Detection-using-LiDAR

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Obstacle Detection using LiDAR

Implemented a Obstacle Detection pipeline using LiDAR Point Cloud segmentation, 3D RANSAC, KD-Tree and Euclidean clustering algorithm.



Files

  • src
    • render
      • box.h: Defines the structure of the box objects.
      • render.h, render.cpp: Defines classes and methods for rendering objects.
    • sensors
      • lidar.h: Functions using ray casting for creating PCD
    • environment.cpp: main file for using PCL viewer and processing/visualizing PCD
    • processPointClouds.h, processPointClouds.cpp: Functions for filtering, segmenting, clustering, boxing, loading and saving PCD


Important Dependencies

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

Build Instructions

cd ~/Obstacle-Detection-using-LiDAR
mkdir build && cd build
cmake ..
make
./environment

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Object Detection pipeline implemented using the Voxel Grid and ROI based filtering, 3D RANSAC segmentation, Euclidean clustering based on KD-Tree, and bounding boxes, by processing Point Cloud data from LiDAR sensor.

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