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slam14(1) v1_4_3代码 SLAM中的优化、李群李代数和BA_李代数轨迹误差计算

作者:互联网

 

安装库环境

https://www.cnblogs.com/gooutlook/p/16442302.html

 

 

 

CMakeLists.txt 

cmake_minimum_required(VERSION 3.0)
project(useSophus)

# 1-1 为使用 sophus,需要使用find_package命令找到它
find_package(Sophus REQUIRED)
include_directories(${Sophus_INCLUDE_DIRS})

# 1-2 Eigen
include_directories("/usr/local/include/eigen3")



# 3-1 李代书基本转换工程
add_subdirectory(example1_李代数基本转换)

# 3-2 轨迹转换为李代书求解误差
add_subdirectory(example2_轨迹误差计算)

  example1_李代数基本转换

 

 

 

 

 

 CMakeLists.txt

# 2 编译程序
add_executable(useSophus useSophus.cpp)
target_link_libraries(useSophus ${Sophus_LIBRARIES} fmt)

useSophus.cpp

#include <iostream>
#include <cmath>
#include <Eigen/Core>
#include <Eigen/Geometry>
#include "sophus/se3.hpp"

using namespace std;
using namespace Eigen;

/// 本程序演示sophus的基本用法

int main(int argc, char **argv) {

  // 沿Z轴转90度的旋转矩阵
  Matrix3d R = AngleAxisd(M_PI / 2, Vector3d(0, 0, 1)).toRotationMatrix();
  // 或者四元数
  Quaterniond q(R);
  Sophus::SO3d SO3_R(R);              // Sophus::SO3d可以直接从旋转矩阵构造
  Sophus::SO3d SO3_q(q);              // 也可以通过四元数构造
  // 二者是等价的
  cout << "SO(3) from matrix:\n" << SO3_R.matrix() << endl;
  cout << "SO(3) from quaternion:\n" << SO3_q.matrix() << endl;
  cout << "they are equal" << endl;

  // 使用对数映射获得它的李代数
  Vector3d so3 = SO3_R.log();
  cout << "so3 = " << so3.transpose() << endl;
  // hat 为向量到反对称矩阵
  cout << "so3 hat=\n" << Sophus::SO3d::hat(so3) << endl;
  // 相对的,vee为反对称到向量
  cout << "so3 hat vee= " << Sophus::SO3d::vee(Sophus::SO3d::hat(so3)).transpose() << endl;

  // 增量扰动模型的更新
  Vector3d update_so3(1e-4, 0, 0); //假设更新量为这么多
  Sophus::SO3d SO3_updated = Sophus::SO3d::exp(update_so3) * SO3_R;
  cout << "SO3 updated = \n" << SO3_updated.matrix() << endl;

  cout << "*******************************" << endl;
  // 对SE(3)操作大同小异
  Vector3d t(1, 0, 0);           // 沿X轴平移1
  Sophus::SE3d SE3_Rt(R, t);           // 从R,t构造SE(3)
  Sophus::SE3d SE3_qt(q, t);            // 从q,t构造SE(3)
  cout << "SE3 from R,t= \n" << SE3_Rt.matrix() << endl;
  cout << "SE3 from q,t= \n" << SE3_qt.matrix() << endl;
  // 李代数se(3) 是一个六维向量,方便起见先typedef一下
  typedef Eigen::Matrix<double, 6, 1> Vector6d;
  Vector6d se3 = SE3_Rt.log();
  cout << "se3 = " << se3.transpose() << endl;
  // 观察输出,会发现在Sophus中,se(3)的平移在前,旋转在后.
  // 同样的,有hat和vee两个算符
  cout << "se3 hat = \n" << Sophus::SE3d::hat(se3) << endl;
  cout << "se3 hat vee = " << Sophus::SE3d::vee(Sophus::SE3d::hat(se3)).transpose() << endl;

  // 最后,演示一下更新
  Vector6d update_se3; //更新量
  update_se3.setZero();
  update_se3(0, 0) = 1e-4;
  Sophus::SE3d SE3_updated = Sophus::SE3d::exp(update_se3) * SE3_Rt;
  cout << "SE3 updated = " << endl << SE3_updated.matrix() << endl;

  return 0;
}

  

 

 

example2_轨迹误差计算 

 

 

 

 

 

 CMakeLists.txt

option(USE_UBUNTU_20 "Set to ON if you are using Ubuntu 20.04" ON)
find_package(Pangolin REQUIRED)
if(USE_UBUNTU_20)
    message("You are using Ubuntu 20.04, fmt::fmt will be linked")
    find_package(fmt REQUIRED)
    set(FMT_LIBRARIES fmt::fmt)
endif()
include_directories(${Pangolin_INCLUDE_DIRS})
add_executable(trajectoryError trajectoryError.cpp)
target_link_libraries(trajectoryError ${Pangolin_LIBRARIES} ${FMT_LIBRARIES})

  trajectoryError.cpp

#include <iostream>
#include <fstream>
#include <unistd.h>
#include <pangolin/pangolin.h>
#include <sophus/se3.hpp>

using namespace Sophus;
using namespace std;

string groundtruth_file = "../../example/groundtruth.txt";
string estimated_file = "../../example/estimated.txt";

typedef vector<Sophus::SE3d, Eigen::aligned_allocator<Sophus::SE3d>> TrajectoryType;

void DrawTrajectory(const TrajectoryType &gt, const TrajectoryType &esti);

TrajectoryType ReadTrajectory(const string &path);

int main(int argc, char **argv) {
  // 1 读取txt轨迹,然后转化成李代数
  TrajectoryType groundtruth = ReadTrajectory(groundtruth_file);
  TrajectoryType estimated = ReadTrajectory(estimated_file);
  assert(!groundtruth.empty() && !estimated.empty());
  assert(groundtruth.size() == estimated.size());
  
  // 2 计算绝对轨迹误差  计算每个李代数的均方根误差 旋转和位移
  // compute rmse  1/N * 累计求和 开平方([log(评估轨迹/真实轨迹)]^2)
  double rmse = 0;
  for (size_t i = 0; i < estimated.size(); i++) {
    Sophus::SE3d p1 = estimated[i], p2 = groundtruth[i]; // 
    double error = (p2.inverse() * p1).log().norm();     //  N_i = log(estimated[i]/groundtruth[i])
    rmse += error * error;                               //  N_error = N_error + N_i
  }
  rmse = rmse / double(estimated.size());                // N_error_mean= N_error / N
  rmse = sqrt(rmse);
  cout << "平均误差 = " << rmse << endl;

  DrawTrajectory(groundtruth, estimated);
  return 0;
}

TrajectoryType ReadTrajectory(const string &path) {
  ifstream fin(path);
  TrajectoryType trajectory;
  if (!fin) {
    cerr << "trajectory " << path << " not found." << endl;
    return trajectory;
  }

  while (!fin.eof()) {
    double time, tx, ty, tz, qx, qy, qz, qw;
    fin >> time >> tx >> ty >> tz >> qx >> qy >> qz >> qw;
    Sophus::SE3d p1(Eigen::Quaterniond(qw, qx, qy, qz), Eigen::Vector3d(tx, ty, tz));
    trajectory.push_back(p1);
  }
  return trajectory;
}

void DrawTrajectory(const TrajectoryType &gt, const TrajectoryType &esti) {
  // create pangolin window and plot the trajectory
  pangolin::CreateWindowAndBind("Trajectory Viewer", 1024, 768);
  glEnable(GL_DEPTH_TEST);
  glEnable(GL_BLEND);
  glBlendFunc(GL_SRC_ALPHA, GL_ONE_MINUS_SRC_ALPHA);

  pangolin::OpenGlRenderState s_cam(
      pangolin::ProjectionMatrix(1024, 768, 500, 500, 512, 389, 0.1, 1000),
      pangolin::ModelViewLookAt(0, -0.1, -1.8, 0, 0, 0, 0.0, -1.0, 0.0)
  );

  pangolin::View &d_cam = pangolin::CreateDisplay()
      .SetBounds(0.0, 1.0, pangolin::Attach::Pix(175), 1.0, -1024.0f / 768.0f)
      .SetHandler(new pangolin::Handler3D(s_cam));


  while (pangolin::ShouldQuit() == false) {
    glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT);

    d_cam.Activate(s_cam);
    glClearColor(1.0f, 1.0f, 1.0f, 1.0f);

    glLineWidth(2);
    for (size_t i = 0; i < gt.size() - 1; i++) {
      glColor3f(0.0f, 0.0f, 1.0f);  // blue for ground truth
      glBegin(GL_LINES);
      auto p1 = gt[i], p2 = gt[i + 1];
      glVertex3d(p1.translation()[0], p1.translation()[1], p1.translation()[2]);
      glVertex3d(p2.translation()[0], p2.translation()[1], p2.translation()[2]);
      glEnd();
    }

    for (size_t i = 0; i < esti.size() - 1; i++) {
      glColor3f(1.0f, 0.0f, 0.0f);  // red for estimated
      glBegin(GL_LINES);
      auto p1 = esti[i], p2 = esti[i + 1];
      glVertex3d(p1.translation()[0], p1.translation()[1], p1.translation()[2]);
      glVertex3d(p2.translation()[0], p2.translation()[1], p2.translation()[2]);
      glEnd();
    }
    pangolin::FinishFrame();
    usleep(5000);   // sleep 5 ms
  }

}

  

 

 estimated.txt

1305031526.67147303 0 0 0 0 0 1 0
1305031526.70754695 0.00288319499999999986 -0.00466209999999999975 -0.00225430399999999994 0.0106974147769499078 0.00218949395434721002 0.999875286151759579 0.0114098017620960397
1305031526.77148104 0.0139789660000000007 -0.0130823169999999996 -0.0108695960000000005 0.0325266728104138744 0.00326054208123759126 0.998528028878688856 0.0432800180783373817
1305031526.80745506 -0.00160120899999999992 -0.0114045459999999998 -0.0268415289999999994 0.0520713221589042333 0.00091570100279441259 0.995935085039260204 0.0734918042242723585
1305031526.87144589 -0.00442845599999999989 -0.00133393800000000001 -0.042973492000000002 0.0708477644042826288 -0.00660150894449177718 0.990774146669160416 0.115341253030162946
1305031526.90748405 -0.00648750300000000037 -0.00346477100000000017 -0.0581952629999999971 0.0812489619239377819 -0.0103815121180553093 0.987398218228383806 0.13540896953982845
1305031526.93961811 -0.0143312160000000008 -0.0136600920000000004 -0.078787915 0.0919272047025398997 -0.0151385086097317149 0.98462591565764801 0.147756626951174169
1305031526.97150993 -0.0186255030000000016 -0.0154946430000000007 -0.0890801999999999983 0.100416749463879573 -0.0174304809069394627 0.980948681762759955 0.16538542411701479
1305031527.00759506 -0.0200488230000000003 -0.00554057199999999968 -0.09790016 0.106515354426187481 -0.0179266069034269629 0.977364594734805747 0.181910871020021708
1305031527.03951192 -0.0234353750000000013 -0.00914814700000000057 -0.106970325000000005 0.113224901561134988 -0.0190224740942741034 0.973931019826736577 0.195644155969599248
1305031527.07148695 -0.0268078479999999987 0.00442536900000000018 -0.112307227999999995 0.120352435034275659 -0.0205858115190009384 0.969181800434609442 0.213958298394227864
1305031527.10749793 -0.0310707279999999988 0.00486450300000000006 -0.118102825999999994 0.125381435967296451 -0.0190312553878885585 0.966949073899550759 0.221194021885630032
1305031527.13947296 -0.030610107000000001 0.0082340759999999999 -0.122761726000000002 0.129590194799126229 -0.0204090004958983259 0.963641381198087954 0.2327770232504148
1305031527.17154002 -0.0329938119999999974 0.0180318879999999994 -0.13112579299999999 0.133578794805705448 -0.0192605694045513733 0.961256464190349047 0.240357538048499325
1305031527.20758796 -0.0367447099999999999 0.0205004740000000012 -0.137645884999999996 0.135950847336720138 -0.01729613691561524 0.961076970311077416 0.239894284829598708
1305031527.27150106 -0.0434453259999999994 0.041889957999999998 -0.136677355 0.137271211321250725 -0.0131129860306878343 0.962857487253339639 0.232142463543274391
1305031527.30747199 -0.046960509999999997 0.043947174999999998 -0.127684563000000001 0.13604061305283241 -0.0112199010043573331 0.965305805374883863 0.222602263086449287
1305031527.33963609 -0.045433633000000001 0.0504947980000000005 -0.122109099999999998 0.131844017608473529 -0.00687977518829437345 0.967865672489769735 0.214023044857652889
1305031527.53974104 -0.0406702160000000021 0.0686886089999999977 -0.0942803470000000005 0.128989607702871434 0.00739045104027097159 0.979327982336411607 0.155639857848090091
1305031527.67154694 -0.0597790590000000024 0.0861703830000000032 -0.0800275800000000009 0.124875025233463388 0.00804662107948116503 0.987459490753712754 0.096256085950777448
1305031527.70752001 -0.0629779169999999944 0.0803183019999999942 -0.0754213999999999996 0.118124430540813299 0.0106047177792244936 0.989585557398202087 0.0815756303017085849
1305031527.77146101 -0.0639681 0.0794579009999999974 -0.0706760210000000055 0.104486306413013499 0.0133050116705799373 0.992445444427947798 0.0629096834424131141
1305031527.80758095 -0.0671490579999999976 0.0713680240000000021 -0.0722353379999999962 0.096843057253499204 0.0147752011912445098 0.993918610864899521 0.0502882756509120607
1305031527.83960104 -0.0841456649999999945 0.0833857800000000066 -0.0651093499999999964 0.0916426060927993946 0.0160889281384837095 0.994409388752208856 0.0499274143265291334
1305031527.87146401 -0.0948576780000000008 0.0900550930000000027 -0.0660034570000000015 0.0808715809044888573 0.012684623828170212 0.995654092512565647 0.0444051323984745172
1305031527.9074769 -0.0992609259999999993 0.085766435000000002 -0.0582616850000000006 0.0672022532098003056 0.00914133975648400224 0.997098538438308757 0.0345658460792010042
1305031527.9395659 -0.114897556999999997 0.077028573000000003 -0.0580298009999999989 0.0548280499089733517 0.000756203026329067504 0.998086069750820482 0.0285921399955056492
1305031527.97150207 -0.125279635 0.0829155149999999952 -0.0613153950000000017 0.0383147708757181835 -0.0131664019572920749 0.998632487760730658 0.0330420728928212601
1305031528.03956008 -0.149975642999999992 0.084671959000000005 -0.0529502440000000005 0.00443556498217298045 -0.0460197808150414819 0.998252268987915192 0.0368091428520600436
1305031528.07154608 -0.160694851999999999 0.0878616420000000037 -0.0553810630000000012 -0.0121078120029755997 -0.0616678520151554069 0.997076631245040201 0.0434588120106803796
1305031528.10751295 -0.176289975999999987 0.0907570419999999961 -0.0554222200000000012 -0.0278695134746601847 -0.0779404133274437388 0.995353715952387086 0.049189046837764247
1305031528.13951302 -0.182451799999999997 0.0963398589999999999 -0.0556840970000000018 -0.0475689564339222756 -0.0964697999079930507 0.992633968922034593 0.055754603680671054
1305031528.17152309 -0.189394832000000013 0.0977570489999999986 -0.0567052590000000009 -0.0622204376415005159 -0.113738763000657744 0.989785101112525378 0.0593090595646924984
1305031528.20752692 -0.202431262000000001 0.0955468859999999975 -0.0615021209999999999 -0.071393286867100933 -0.133537348010525664 0.986344009532632571 0.064778624064716267
1305031528.23949289 -0.211042851000000004 0.0982791189999999981 -0.066208452000000001 -0.080469144557817493 -0.14846986233909637 0.9828485073852119 0.0740960746720367991
1305031528.27544999 -0.223710716000000004 0.104189500000000004 -0.0732756999999999992 -0.0931655297700753476 -0.159726720176927739 0.979374444558591795 0.081444805050611857
1305031528.30766511 -0.244773716000000002 0.107162124999999997 -0.0782576799999999961 -0.112386963377767449 -0.174391135034480171 0.974128121606721131 0.0896175495038307318
1305031528.33959293 -0.267502815000000005 0.119377657999999998 -0.0798698739999999935 -0.131352675383011236 -0.185550596303205667 0.968630652701606976 0.100359900000491956
1305031528.37543511 -0.277227013999999994 0.122483246000000004 -0.0864017980000000019 -0.156168524265171388 -0.190717032208737369 0.963421550312331787 0.105153801871891051
1305031528.40745902 -0.286472469999999979 0.125400484000000007 -0.0964349729999999933 -0.185297354247303009 -0.197440161197977665 0.956370767115123144 0.10971430655432915
1305031528.4394691 -0.294514090000000006 0.129935055999999993 -0.100482926 -0.217375927680142328 -0.202731514105905258 0.948014827437984153 0.113646496695909746
1305031528.47534204 -0.309931367999999985 0.129882887000000002 -0.129739462999999999 -0.24096266088637508 -0.210141203005541011 0.940934326890584094 0.111446235184977191
1305031528.53962588 -0.329695432999999982 0.143418118000000011 -0.155989199999999995 -0.298002071092221266 -0.219036376212898121 0.922438881953339451 0.111015046642073137
1305031528.57544899 -0.329237043999999979 0.149565578000000005 -0.154559985000000011 -0.328091901581889445 -0.223088823875202291 0.911774012186304383 0.106090675745080187
1305031528.60784101 -0.340293765000000026 0.151535481 -0.160942093000000008 -0.350554531114140921 -0.228903222339404411 0.902669204168785821 0.0995145384517894666
1305031528.63948703 -0.340425193000000015 0.165493459000000009 -0.169357075999999995 -0.372110553224519525 -0.231576029496044361 0.893679457350770257 0.0961421148665973918
1305031528.70744705 -0.339044899000000011 0.179300487000000008 -0.177713453999999993 -0.396103607091124132 -0.245452708381115436 0.881343811052284765 0.0780255542601921981
1305031528.739609 -0.342159747999999986 0.189583003999999999 -0.177449270999999992 -0.403506330614875119 -0.253377461734651033 0.876330118789288237 0.0709085745264735473
1305031528.77544308 -0.341898619999999986 0.206203520000000001 -0.174872875000000011 -0.407845536524386654 -0.261114782097794207 0.872509559375651222 0.0648703145149116672
1305031528.80755901 -0.34840449699999998 0.222904055999999989 -0.195781290999999996 -0.403348861727644892 -0.271214007144461366 0.872102198248972837 0.0564837488218232556
1305031528.83957195 -0.353144257999999989 0.236203671000000004 -0.200213342999999988 -0.405389349480667072 -0.278263248194033253 0.869577847481488364 0.0454247421743811178
1305031528.87543297 -0.357103824999999986 0.256649940999999993 -0.206021338999999998 -0.406093620986040627 -0.28625493776076677 0.867065417159374352 0.0366557494571542672
1305031528.9075191 -0.363840311999999999 0.273261279000000024 -0.213352873999999998 -0.40329343650345828 -0.293037358998872122 0.866551291825208292 0.0239242333264769735
1305031528.9396019 -0.373810409999999982 0.280675173 -0.220558762999999991 -0.399947995371204845 -0.29945343278554698 0.866218256809002218 0.0059307809165312711
1305031528.97546506 -0.383288622000000023 0.293885916000000025 -0.225069121000000011 -0.398547528123664618 -0.297109453601228368 0.867656762915129387 -0.00758829196427482366
1305031529.00748706 -0.387591242999999974 0.312122375000000007 -0.228814498000000005 -0.395879656360808685 -0.293224841267248071 0.869930744792863586 -0.0227813390207631389
1305031529.03949404 -0.39693099300000001 0.326887786000000014 -0.233368337000000009 -0.387588739038389252 -0.290734917028796225 0.873871803086553767 -0.0399530870039572078
1305031529.07542205 -0.409061730000000012 0.334867179000000015 -0.235107093999999989 -0.375858518808178221 -0.282847496113258434 0.880569345579059859 -0.0576653744308393765
1305031529.10752296 -0.420381188000000017 0.34899413600000001 -0.244129315000000013 -0.359931793943662981 -0.270585921702337528 0.890104286441738446 -0.0703329364027725223
1305031529.13959694 -0.43247598399999998 0.367254644999999991 -0.253846556000000001 -0.344622522292981404 -0.252671926481624842 0.900152502153270584 -0.0843663888269161055
1305031529.17541099 -0.442705750000000009 0.384032130000000027 -0.264635383999999974 -0.33028602954052666 -0.234458076513291508 0.908912250743155292 -0.0990912180622159772
1305031529.20746589 -0.461968184000000004 0.417857319000000005 -0.283306687999999973 -0.308710959981694733 -0.220306736846033774 0.919035187014479282 -0.107409542949843603
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#include<iostream> #include<fstream> #include<unistd.h> #include<pangolin/pangolin.h> #include<sophus/se3.hpp>
using namespace Sophus; using namespace std;
string groundtruth_file = "../../example/groundtruth.txt"; string estimated_file = "../../example/estimated.txt";
typedef vector<Sophus::SE3d, Eigen::aligned_allocator<Sophus::SE3d>> TrajectoryType;
void DrawTrajectory(const TrajectoryType &gt, const TrajectoryType &esti);
TrajectoryType ReadTrajectory(const string &path);
int main(int argc, char **argv) { // 1 读取txt轨迹,然后转化成李代数 TrajectoryType groundtruth = ReadTrajectory(groundtruth_file); TrajectoryType estimated = ReadTrajectory(estimated_file); assert(!groundtruth.empty() && !estimated.empty()); assert(groundtruth.size() == estimated.size());   // 2 计算绝对轨迹误差 计算每个李代数的均方根误差 旋转和位移 // compute rmse 1/N * 累计求和 开平方([log(评估轨迹/真实轨迹)]^2) double rmse = 0; for (size_t i = 0; i < estimated.size(); i++) { Sophus::SE3d p1 = estimated[i], p2 = groundtruth[i]; // double error = (p2.inverse() * p1).log().norm(); // N_i = log(estimated[i]/groundtruth[i]) rmse += error * error; // N_error = N_error + N_i } rmse = rmse / double(estimated.size()); // N_error_mean= N_error / N rmse = sqrt(rmse); cout << "平均误差 = " << rmse << endl;
DrawTrajectory(groundtruth, estimated); return 0; }
TrajectoryType ReadTrajectory(const string &path) { ifstream fin(path); TrajectoryType trajectory; if (!fin) { cerr << "trajectory " << path << " not found." << endl; return trajectory; }
while (!fin.eof()) { double time, tx, ty, tz, qx, qy, qz, qw; fin >> time >> tx >> ty >> tz >> qx >> qy >> qz >> qw; Sophus::SE3d p1(Eigen::Quaterniond(qw, qx, qy, qz), Eigen::Vector3d(tx, ty, tz)); trajectory.push_back(p1); } return trajectory; }
void DrawTrajectory(const TrajectoryType &gt, const TrajectoryType &esti) { // create pangolin window and plot the trajectory pangolin::CreateWindowAndBind("Trajectory Viewer", 1024, 768); glEnable(GL_DEPTH_TEST); glEnable(GL_BLEND); glBlendFunc(GL_SRC_ALPHA, GL_ONE_MINUS_SRC_ALPHA);
pangolin::OpenGlRenderState s_cam( pangolin::ProjectionMatrix(1024, 768, 500, 500, 512, 389, 0.1, 1000), pangolin::ModelViewLookAt(0, -0.1, -1.8, 0, 0, 0, 0.0, -1.0, 0.0) );
pangolin::View &d_cam = pangolin::CreateDisplay() .SetBounds(0.0, 1.0, pangolin::Attach::Pix(175), 1.0, -1024.0f / 768.0f) .SetHandler(new pangolin::Handler3D(s_cam));

while (pangolin::ShouldQuit() == false) { glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT);
d_cam.Activate(s_cam); glClearColor(1.0f, 1.0f, 1.0f, 1.0f);
glLineWidth(2); for (size_t i = 0; i < gt.size() - 1; i++) { glColor3f(0.0f, 0.0f, 1.0f); // blue for ground truth glBegin(GL_LINES); auto p1 = gt[i], p2 = gt[i + 1]; glVertex3d(p1.translation()[0], p1.translation()[1], p1.translation()[2]); glVertex3d(p2.translation()[0], p2.translation()[1], p2.translation()[2]); glEnd(); }
for (size_t i = 0; i < esti.size() - 1; i++) { glColor3f(1.0f, 0.0f, 0.0f); // red for estimated glBegin(GL_LINES); auto p1 = esti[i], p2 = esti[i + 1]; glVertex3d(p1.translation()[0], p1.translation()[1], p1.translation()[2]); glVertex3d(p2.translation()[0], p2.translation()[1], p2.translation()[2]); glEnd(); } pangolin::FinishFrame(); usleep(5000); // sleep 5 ms }
}

标签:p2,p1,BA,pangolin,slam14,v1,include,translation,estimated
来源: https://www.cnblogs.com/gooutlook/p/16442572.html