Lidar Obstacle Detection

A comprehensive 3D object detection system using LiDAR point cloud data, implemented as part of the Udacity Sensor Fusion Nanodegree program. This project demonstrates real-time obstacle detection and tracking in autonomous driving scenarios through custom implementations of fundamental computer vision algorithms.

Project Overview

This project implements a complete LiDAR-based obstacle detection pipeline that processes point cloud data to identify and track vehicles and other obstacles in a 3D environment. The system is designed to work with real-world LiDAR data and provides robust detection capabilities suitable for autonomous driving applications.

Key Features

• Real-time Point Cloud Processing: Efficient filtering and preprocessing of LiDAR point cloud data
• Custom 3D RANSAC Implementation: Ground plane segmentation using a from-scratch RANSAC algorithm
• KD-Tree Data Structure: Custom 3D KD-Tree for efficient nearest neighbor searches
• Euclidean Clustering: Custom clustering algorithm for object detection and grouping
• Bounding Box Generation: Automatic generation of 3D bounding boxes around detected objects
• Multi-frame Object Tracking: Consistent detection and tracking of objects across video frames

Technical Implementation

Core Algorithms

1. 3D RANSAC Plane Segmentation

• Purpose: Separates ground plane from obstacle points
• Implementation: Custom RANSAC algorithm that iteratively fits planes to point cloud data
• Key Features:
  • Handles 3D point cloud data
  • Configurable iteration count and distance threshold
  • Robust to outliers and noise

2. KD-Tree for Spatial Indexing

• Purpose: Efficient spatial data structure for nearest neighbor searches
• Implementation: Custom 3D KD-Tree with insert and search operations
• Key Features:
  • Balanced binary tree structure
  • O(log n) search complexity
  • Supports 3D euclidean distance queries

3. Euclidean Clustering

• Purpose: Groups nearby points into distinct object clusters
• Implementation: Custom clustering algorithm using KD-Tree for neighbor searches
• Key Features:
  • Configurable cluster size limits
  • Distance-based grouping
  • Efficient cluster formation and validation

Processing Pipeline

1. Point Cloud Loading: Read LiDAR data from PCD files
2. Filtering: Remove noise and limit processing region
3. Ground Segmentation: Separate ground plane using RANSAC
4. Clustering: Group obstacle points into distinct objects
5. Bounding Box Generation: Create 3D boxes around detected objects
6. Visualization: Render results with color-coded objects

Project Structure

SFND_Lidar_Obstacle_Detection/
├── src/
│   ├── environment.cpp          # Main application and visualization
│   ├── processPointClouds.cpp   # Core point cloud processing algorithms
│   ├── processPointClouds.h     # Point cloud processing header
│   ├── kdtree.h                 # Custom KD-Tree implementation
│   ├── render/                  # Visualization and rendering utilities
│   ├── sensors/                 # LiDAR sensor simulation and data
│   └── quiz/                    # Algorithm development and testing
├── build/                       # Build directory
├── media/                       # Demo images and videos
└── CMakeLists.txt              # Build configuration