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SFND_Lidar_Obstacle_Detection

Sensor Fusion Nanodegree Project

Udacity Sensor Fusion P1 on Lidar Obstacle Detection

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3D LiDAR obstacle detection on point cloud data using segmentation and clustering.

C++, PCL, RNASAC, KdTree

Sensor fusion of LiDAR, camera, and radar for autonomous vehicles using C++

ransac for segment, kt_tree for clustering, VoxelGrid to filter pcd, CropBox to crop pcd, streamPcd

Lidar project for obstacle detection using PointCloud library.

Use lidar car data to detect incoming road obstacles track multiple cars on the road, estimating their positions and speed

Use RANSAC plane segmentation, three dimension KD tree and Euclidean Clutering to detect obstables using lidar data.

Udacity -Sensor Fusion - Project 1

Udacity exercize to detect car in lidar pointcloud

Basic full pipeline from streaming raw point cloud to detecting objects

Udacity SFND Lidar Obstacle Detection project. Lidar PCD stream visualization, RANSAC, KDTree, Bboxes, and various other algorithm implementations

SFND_Lidar_Obstacle_Detection

My implementation of LiDAR Object Detection project - Sensor Fusion Engineer Nanodegree - Udacity

Process lidar point cloud data to detect cars and trucks on a narrow street

Sensor Fusion Nanodegree | Lidar Obstacle Detection in Autonomous Vehicles

Nanodegree self driving car

SFND_Lidar_Obstacle_Detection

My solution for SFND_Lidar_Obstacle_Detection

Exercises for the Lidar class within the Sensor Fusion Nanodegree program from Udacity

Implemented RANSAC plane model fitting and K-D Tree based euclidean cluster extraction. Also integrated PCL functionalities for downsampling, filtering, segmentation and clustering.

Lidar point cloud processing project. This project performs processing of Lidar point cloud information to determine obstacles. This is done by performing segmentation to determine which points belong to the same 3d plane and clustering to determine which points represent an obstacle on the road

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• Developed point cloud object detection pipeline and implemented RANSAC for segmentation, KD-Tree and Euclidean clustering algorithms from scratch. • Verified results of segmentation and clustering with PCL functions and used PCL visualizer for displaying the results for both the methods.

LiDAR Obstacle Detection Project in the Udacity Sensor Fusion Nanodegree Program

Udacity Sensor Fusion Nanodegree Project 1 (Lidar Obstacle Detection)