💻
Tutorial
  • INTRO
  • Part 0 (개요)
    • README
    • 3D 영상처리
    • [별첨] PCL & PCD란 (100%)
    • chapter02 : PCL 설치 (100%)
    • chapter03 : ROS 실습 준비(100%)
  • Part 1 (초급)
    • README
    • PCL 기반 로봇 비젼
    • [별첨] 파일 생성 및 입출력 (70%)
      • PCL-Cpp (70%)
      • PCL-Python (70%)
      • Open3D-Python (70%)
      • ROS 실습 (90%)
    • Filter
    • [별첨] 샘플링 (70%)
      • 다운샘플링-PCL-Cpp (70%)
      • 다운샘플링-PCL-Python (50%)
      • 업샘플링-PCL-Cpp (70%)
      • ROS 실습 (90%)
    • [별첨] 관심 영역 설정 (70%)
      • PCL-Cpp (70%)
      • PCL-Python (70%)
      • ROS 실습 (90%)
    • [별첨] 노이즈 제거 (70%)
      • PCL-Cpp (70%)
      • PCL-Python (50%)
      • ROS 실습 (90%)
  • Part 2 (중급)
    • README
    • Kd-Tree/Octree Search
    • Chapter03 : Sample Consensus
    • [별첨] 바닥제거 (RANSAC) (70%)
      • PCL-Cpp (70%)
      • PCL-Python (70%)
      • ROS 실습 (90%)
    • 군집화 (70%)
      • Euclidean-PCL-Cpp (70%)
      • Euclidean-PCL-Python (0%)
      • Conditional-Euclidean-PCL-Cpp (50%)
      • DBSCAN-PCL-Python (0%)
      • Region-Growing-PCL-Cpp (50%)
      • Region-Growing-RGB-PCL-Cpp (50%)
      • Min-Cut-PCL-Cpp (50%)
      • Model-Outlier-Removal-PCL-Cpp (50%)
      • Progressive-Morphological-Filter-PCL-Cpp (50%)
    • 포인트 탐색과 배경제거 (60%)
      • Search-Octree-PCL-Cpp (70%)
      • Search-Octree-PCL-Python (70%)
      • Search-Kdtree-PCL-Cpp (70%)
      • Search-Kdtree-PCL-Python (70%)
      • Compression-PCL-Cpp (70%)
      • DetectChanges-PCL-Cpp (50%)
      • DetectChanges-PCL-Python (50%)
    • 특징 찾기 (50%)
      • PFH-PCL-Cpp
      • FPFH-PCL-Cpp
      • Normal-PCL-Cpp (70%)
      • Normal-PCL-Python (80%)
      • Tmp
    • 분류/인식 (30%)
      • 인식-GeometricConsistencyGrouping
      • SVM-RGBD-PCL-Python (70%)
      • SVM-LIDAR-PCL-Python (0%)
      • SVM-ROS (0%)
    • 정합 (70%)
      • ICP-PCL-Cpp (70%)
      • ICP-ROS 실습 (10%)
    • 재구성 (30%)
      • Smoothig-PCL-Cpp (70%)
      • Smoothig-PCL-Python (70%)
      • Triangulation-PCL-Cpp (70%)
  • Part 3 (고급)
    • README
    • 딥러닝 기반 학습 데이터 생성 (0%)
      • PointGAN (90%)
      • AutoEncoder (0%)
    • 딥러닝 기반 샘플링 기법 (0%)
      • DenseLidarNet (50%)
      • Point Cloud Upsampling Network
      • Pseudo-LiDAR
    • 딥러닝 기반 자율주행 탐지 기술 (0%)
    • 딥러닝 기반 자율주행 분류 기술 (0%)
      • Multi3D
      • PointNet
      • VoxelNet (50%)
      • YOLO3D
      • SqueezeSeg
      • butNet
  • Snippets
    • PCL-Snippets
    • PCL-Python-Helper (10%)
    • Lidar Data Augmentation
  • Appendix
    • 시각화Code
    • 시각화툴
    • Annotation툴
    • Point Cloud Libraries (0%)
    • 데이터셋
    • Cling_PCL
    • 참고 자료
    • 작성 계획_Tips
    • 용어집
Powered by GitBook
On this page

Was this helpful?

  1. Part 2 (중급)
  2. 정합 (70%)

ICP-ROS 실습 (10%)

#!/usr/bin/env python3
# coding: utf-8

import sys
sys.path.append("/workspace/include")

import rospy
from sensor_msgs.msg import PointCloud2
import sensor_msgs.point_cloud2 as pc2

import numpy as np
import pcl
import pcl_msg

import pcl_helper
import filter

import time

def icp(input_pcl):



    # 입력 포인트 #cloudB cloudA

    lidar_202 = pcl_helper.XYZRGB_to_XYZ(input_pcl)

    a202 = lidar_202.to_array()
    ones = np.ones((a202.shape[0],1))
    a202 = np.column_stack([a202, ones])


    key_array = np.array([[-0.986734, 0.070747, -0.146117, 13.072186],
                     [-0.051031, -0.989596, -0.134524, 0.532883],
                     [-0.154114, -0.125283, 0.980078, 1.061544],
                     [0.000000, 0.000000, 0.000000, 1.000000]])


    new_data =  np.ones((a202.shape[0],a202.shape[1]), dtype='f')

    for I in range(0,a202.shape[0]-1):
        new_data[I,] = np.dot(key_array, a202[I,])

    new_data = np.delete(new_data, (3), axis=1)
    new_data = np.delete(new_data, (new_data.shape[0]-1), axis=0)

    new_cloud = pcl.PointCloud()
    new_cloud.from_array(new_data)





    new_cloud = pcl_helper.XYZ_to_XYZRGB(new_cloud,[255,255,255])
    print("[{}]cloud type :{}".format(time.time(),type(new_cloud)))
    return new_cloud

def callback(input_ros_msg):

    pcl_xyzrgb = pcl_helper.ros_to_pcl(input_ros_msg) #ROS 메시지를 PCL로 변경    
    calibrated_pcl = icp(pcl_xyzrgb) # 탐지 영역(RoI) 설정 
    roi_ros_msg = pcl_helper.pcl_to_ros(calibrated_pcl) #PCL을 ROS 메시지로 변경 
    pub = rospy.Publisher("/velodyne_icp", PointCloud2, queue_size=1)
    pub.publish(roi_ros_msg)


if __name__ == "__main__":

    rospy.init_node('myopen3d_node', anonymous=True)
    rospy.Subscriber('/lidar_202/velodyne_points', PointCloud2, callback)    

    rospy.spin()
PreviousICP-PCL-Cpp (70%)Next재구성 (30%)

Last updated 5 years ago

Was this helpful?