heyijia / vins-course

VINS-Mono code without Ceres or ROS

Vins Course

作者：贺一家，高翔，崔华坤，赵松

描述： 这是一个用于深蓝学院教学的代码，她基于 VINS-Mono 框架，但不依赖 ROS, Ceres, G2o。这个代码非常基础，目的在于演示仅基于 Eigen 的后端 LM 算法，滑动窗口算法，鲁棒核函数等等 SLAM 优化中常见的算法。 该代码支持 Ubuntu or Mac OS.

安装依赖项：

1. pangolin: https://github.com/stevenlovegrove/Pangolin

2. opencv

3. Eigen

4. Ceres: vins 初始化部分使用了 ceres 做 sfm，所以我们还是需要依赖 ceres.

编译代码

mkdir vins_course
cd vins_course
git clone https://github.com/HeYijia/VINS-Course
mkdir build 
cd build
cmake ..
make -j4

运行

1. CurveFitting Example to Verify Our Solver.

cd bin
./testCurveFitting 

2. VINs-Mono on Euroc Dataset

cd bin
./run_euroc /home/dataset/EuRoC/MH-05/mav0/ ../config/

3. VINs-Mono on Simulation Dataset (project homework)

you can use this code to generate vio data.

https://github.com/HeYijia/vio_data_simulation

4. Validation Results

evo package

evo_ape euroc euroc_mh05_groundtruth.csv pose_output.txt -a -p

Licence

The source code is released under GPLv3 license.

We are still working on improving the code reliability. For any technical issues, please contact Yijia He [email protected] , Xiang Gao https://github.com/gaoxiang12 or Huakun Cuihttps://github.com/StevenCui.

For commercial inquiries, please contact Song Zhao [email protected]

感谢

我们使用了港科大沈老师组的 VINS-Mono 作为基础代码，非常感谢该组的工作。

vins-course's Issues

更新先验b_prior_时，为什么是“减法”？

VINS-Course/src/backend/problem.cc

Line 473 in 75b9586

b_prior_ -= H_prior_ * delta_x_.head(ordering_poses_); // update the error_prior

贺博，你好。请问，更新b_prior_的时候，为什么是“减法”？ b_prior_ -= H_prior_ * delta_x_.head(ordering_poses_);我的理解是“加法”，请问，我哪里出错了？

请问为啥mac上euroc mav数据集会运行一会儿后LM优化挂掉了？

mac系统，数据集是MH_05_difficult, 会运行很短的时间后优化器挂掉，lm算法的lambda值变得很大梯度无法下降。对代码的改动只有把opencv2的宏定义换成opencv4的宏定义，并且检查过应该不会造成影响，关键点一开始也能监测到，请问这个可能的原因是啥？

部分log 如下

/Users/mayday/VINS-Course/bin/run_euroc /Users/mayday/Downloads/mav0/ ../config/
1 System() sConfig_file: ../config/euroc_config.yaml
 fix extrinsic param 
1 readParameters:  
  INIT_DEPTH: 5
  MIN_PARALLAX: 0.0217391
  ACC_N: 0.08
  ACC_W: 4e-05
  GYR_N: 0.004
  GYR_W: 2e-06
  RIC:    0.0148655  -0.999881  0.0041403
  0.999557  0.0149672  0.0257155
-0.0257744 0.00375619   0.999661
  TIC:   -0.0216401  -0.064677 0.00981073
  G:           0       0 9.81007
  BIAS_ACC_THRESHOLD:0.1
  BIAS_GYR_THRESHOLD:0.1
  SOLVER_TIME:0.04
  NUM_ITERATIONS:8
  ESTIMATE_EXTRINSIC:0
  ESTIMATE_TD:0
  ROLLING_SHUTTER:0
  ROW:480
  COL:752
  TD:0
  TR:0
  FOCAL_LENGTH:460
  IMAGE_TOPIC:/cam0/image_raw
  IMU_TOPIC:/imu0
  FISHEYE_MASK:
  CAM_NAMES[0]:../config/euroc_config.yaml
  MAX_CNT:150
  MIN_DIST:30
  FREQ:10
  F_THRESHOLD:1
  SHOW_TRACK:1
  STEREO_TRACK:0
  EQUALIZE:1
  FISHEYE:0
  PUB_THIS_FRAME:0
reading paramerter of camera ../config/euroc_config.yaml
1 PinholeCamera 
  m_cameraName: camera
  m_imageWidth: 752
  m_imageHeight: 480
  m_k1: -0.2917
  m_k2: 0.08228
  m_p1: 5.333e-05
  m_p2: -0.0001578
  m_fx: 461.6
  m_fy: 460.3
  m_cx: 363
  m_cy: 248.1

1 Estimator::setParameter FOCAL_LENGTH: 460
2 System() end
1 ProcessBackEnd start
1 PubImuData start sImu_data_filea: ../config/MH_05_imu0.txt1 PubImageData start sImage_file: 1 PubImageData start sImage_file: 
../config/MH_05_cam0.txt
../config/MH_05_cam0.txt
1 PubImageData skip the first detected feature, which doesn't contain optical flow speed
2 PubImageData first_image_flag
2019-11-16 00:19:25.191527+0800 run_euroc[5569:2277730] MessageTracer: Falling back to default whitelist
4 PubImage init_pub skip the first image!
Not enough features or parallax; Move device around
wait for imu, only should happen at the beginning sum_of_wait: 0
Not enough features or parallax; Move device around
wait for imu, only should happen at the beginning sum_of_wait: 1
Not enough features or parallax; Move device around
wait for imu, only should happen at the beginning sum_of_wait: 2
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wait for imu, only should happen at the beginning sum_of_wait: 6
Not enough features or parallax; Move device around
wait for imu, only should happen at the beginning sum_of_wait: 7
wait for imu, only should happen at the beginning sum_of_wait: 8
Not enough features or parallax; Move device around
wait for imu, only should happen at the beginning sum_of_wait: 9
wait for imu, only should happen at the beginning sum_of_wait: 10
wait for imu, only should happen at the beginning sum_of_wait: 11
Not enough features or parallax; Move device around
wait for imu, only should happen at the beginning sum_of_wait: 12
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Not enough features or parallax; Move device around
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Not enough features or parallax; Move device around
wait for imu, only should happen at the beginning sum_of_wait: 20
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wait for imu, only should happen at the beginning sum_of_wait: 22
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Not enough features or parallax; Move device around
wait for imu, only should happen at the beginning sum_of_wait: 27
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wait for imu, only should happen at the beginning sum_of_wait: 29
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wait for imu, only should happen at the beginning sum_of_wait: 35
Not enough features or parallax; Move device around
wait for imu, only should happen at the beginning sum_of_wait: 36
wait for imu, only should happen at the beginning sum_of_wait: 37
wait for imu, only should happen at the beginning sum_of_wait: 38
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wait for imu, only should happen at the beginning sum_of_wait: 40
Not enough features or parallax; Move device around
wait for imu, only should happen at the beginning sum_of_wait: 41
wait for imu, only should happen at the beginning sum_of_wait: 42
wait for imu, only should happen at the beginning sum_of_wait: 43
wait for imu, only should happen at the beginning sum_of_wait: 44
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wait for imu, only should happen at the beginning sum_of_wait: 46
wait for imu, only should happen at the beginning sum_of_wait: 47
wait for imu, only should happen at the beginning sum_of_wait: 48
wait for imu, only should happen at the beginning sum_of_wait: 49
wait for imu, only should happen at the beginning sum_of_wait: 50
Not enough features or parallax; Move device around
wait for imu, only should happen at the beginning sum_of_wait: 51
wait for imu, only should happen at the beginning sum_of_wait: 52
wait for imu, only should happen at the beginning sum_of_wait: 53
wait for imu, only should happen at the beginning sum_of_wait: 54
wait for imu, only should happen at the beginning sum_of_wait: 55
wait for imu, only should happen at the beginning sum_of_wait: 56
wait for imu, only should happen at the beginning sum_of_wait: 57
wait for imu, only should happen at the beginning sum_of_wait: 58
wait for imu, only should happen at the beginning sum_of_wait: 59
wait for imu, only should happen at the beginning sum_of_wait: 60
Not enough features or parallax; Move device around
wait for imu, only should happen at the beginning sum_of_wait: 61
wait for imu, only should happen at the beginning sum_of_wait: 62
wait for imu, only should happen at the beginning sum_of_wait: 63
wait for imu, only should happen at the beginning sum_of_wait: 64
wait for imu, only should happen at the beginning sum_of_wait: 65
Not enough features or parallax; Move device around
wait for imu, only should happen at the beginning sum_of_wait: 66
wait for imu, only should happen at the beginning sum_of_wait: 67
wait for imu, only should happen at the beginning sum_of_wait: 68
wait for imu, only should happen at the beginning sum_of_wait: 69
wait for imu, only should happen at the beginning sum_of_wait: 70
wait for imu, only should happen at the beginning sum_of_wait: 71
wait for imu, only should happen at the beginning sum_of_wait: 72
wait for imu, only should happen at the beginning sum_of_wait: 73
wait for imu, only should happen at the beginning sum_of_wait: 74
wait for imu, only should happen at the beginning sum_of_wait: 75
Not enough features or parallax; Move device around
wait for imu, only should happen at the beginning sum_of_wait: 76
wait for imu, only should happen at the beginning sum_of_wait: 77
wait for imu, only should happen at the beginning sum_of_wait: 78
wait for imu, only should happen at the beginning sum_of_wait: 79
wait for imu, only should happen at the beginning sum_of_wait: 80
wait for imu, only should happen at the beginning sum_of_wait: 81
wait for imu, only should happen at the beginning sum_of_wait: 82
Not enough features or parallax; Move device around
wait for imu, only should happen at the beginning sum_of_wait: 83
wait for imu, only should happen at the beginning sum_of_wait: 84
wait for imu, only should happen at the beginning sum_of_wait: 85
Not enough features or parallax; Move device around
wait for imu, only should happen at the beginning sum_of_wait: 86
wait for imu, only should happen at the beginning sum_of_wait: 87
wait for imu, only should happen at the beginning sum_of_wait: 88
wait for imu, only should happen at the beginning sum_of_wait: 89
global SFM failed!
wait for imu, only should happen at the beginning sum_of_wait: 90
global SFM failed!
global SFM failed!
global SFM failed!
iter: 0 , chi= 3.21312e+06 , Lambda= 500000
iter: 1 , chi= 8210.04 , Lambda= 333333
iter: 2 , chi= 4502.87 , Lambda= 222222
iter: 3 , chi= 3173.47 , Lambda= 148148
iter: 4 , chi= 2345.79 , Lambda= 98765.4
iter: 5 , chi= 1740.97 , Lambda= 65843.6
iter: 6 , chi= 1275.09 , Lambda= 43895.7
iter: 7 , chi= 970.319 , Lambda= 29263.8
iter: 8 , chi= 800.372 , Lambda= 19509.2
iter: 9 , chi= 725.164 , Lambda= 13006.1
problem solve cost: 204.265 ms
   makeHessian cost: 150.437 ms
edge factor cnt: 578
Initialization finish!
1 BackEnd processImage dt: 318.181000 stamp: 1403638523.027829 p_wi:  0.016292 -0.002303  0.130714
iter: 0 , chi= 745.454920 , Lambda= 500000.000000
iter: 1 , chi= 692.065917 , Lambda= 333333.333333
iter: 2 , chi= 661.554274 , Lambda= 222222.222222
iter: 3 , chi= 644.234796 , Lambda= 148148.148148
iter: 4 , chi= 630.264263 , Lambda= 98765.432099
iter: 5 , chi= 616.299235 , Lambda= 65843.621399
iter: 6 , chi= 600.444146 , Lambda= 43895.747599
iter: 7 , chi= 581.436377 , Lambda= 29263.831733
iter: 8 , chi= 558.663966 , Lambda= 19509.221155
iter: 9 , chi= 532.128932 , Lambda= 13006.147437
problem solve cost: 187.251000 ms
   makeHessian cost: 146.118000 ms
----------- update bprior -------------
             before: 17386.725580
                     10.671542
             after: 14626.155381
                    7.425029
edge factor cnt: 0
1 BackEnd processImage dt: 206.308000 stamp: 1403638523.127830 p_wi:  0.017609 -0.006107  0.168797
iter: 0 , chi= 592.010783 , Lambda= 500000.000000
iter: 1 , chi= 582.011298 , Lambda= 333333.333333
iter: 2 , chi= 578.244231 , Lambda= 222222.222222
iter: 3 , chi= 573.568290 , Lambda= 148148.148148
iter: 4 , chi= 569.382454 , Lambda= 98765.432099
iter: 5 , chi= 564.178063 , Lambda= 65843.621399
iter: 6 , chi= 557.947238 , Lambda= 43895.747599
iter: 7 , chi= 551.057875 , Lambda= 29263.831733
iter: 8 , chi= 544.329040 , Lambda= 19509.221155
iter: 9 , chi= 538.798409 , Lambda= 13006.147437
problem solve cost: 183.791000 ms
   makeHessian cost: 142.183000 ms
----------- update bprior -------------
             before: 14169.987091
                     7.239563
             after: 14003.869986
                    6.498913
edge factor cnt: 470
1 BackEnd processImage dt: 221.172000 stamp: 1403638523.227829 p_wi:  0.014177 -0.012528  0.165095
iter: 0 , chi= 694.372067 , Lambda= 500000.000000
iter: 1 , chi= 601.031408 , Lambda= 333333.333333
iter: 2 , chi= 594.941343 , Lambda= 222222.222222
iter: 3 , chi= 583.987674 , Lambda= 148148.148148
iter: 4 , chi= 576.888224 , Lambda= 98765.432099
iter: 5 , chi= 569.774742 , Lambda= 65843.621399
iter: 6 , chi= 565.930514 , Lambda= 43895.747599
iter: 7 , chi= 562.297411 , Lambda= 29263.831733
iter: 8 , chi= 559.073784 , Lambda= 19509.221155
iter: 9 , chi= 556.178606 , Lambda= 13006.147437
problem solve cost: 213.803000 ms
   makeHessian cost: 161.372000 ms
----------- update bprior -------------
             before: 42414.732696
                     13.576271
             after: 50182.140686
                    13.855544
edge factor cnt: 0
1 BackEnd processImage dt: 232.737000 stamp: 1403638523.327830 p_wi:  0.009445 -0.019707  0.129923
iter: 0 , chi= 618.968552 , Lambda= 500000.000000
iter: 1 , chi= 613.714480 , Lambda= 333333.333333
iter: 2 , chi= 612.751168 , Lambda= 222222.222222
iter: 3 , chi= 611.939101 , Lambda= 148148.148148
iter: 4 , chi= 611.117423 , Lambda= 98765.432099
iter: 5 , chi= 610.941737 , Lambda= 65843.621399
iter: 6 , chi= 610.798154 , Lambda= 43895.747599
iter: 7 , chi= 610.644269 , Lambda= 29263.831733
iter: 8 , chi= 610.486812 , Lambda= 19509.221155
iter: 9 , chi= 610.338178 , Lambda= 13006.147437
problem solve cost: 203.063000 ms
   makeHessian cost: 157.687000 ms
----------- update bprior -------------
             before: 50340.959920
                     13.267921
             after: 51582.647278
                    13.446301
edge factor cnt: 448
1 BackEnd processImage dt: 236.664000 stamp: 1403638523.427830 p_wi:  0.004868 -0.027164  0.066776
iter: 0 , chi= 842.197330 , Lambda= 500000.000000
iter: 1 , chi= 783.428681 , Lambda= 333333.333333
iter: 2 , chi= 780.847695 , Lambda= 222222.222222
iter: 3 , chi= 779.764199 , Lambda= 148148.148148
iter: 4 , chi= 777.817808 , Lambda= 98765.432099
iter: 5 , chi= 776.614725 , Lambda= 65843.621399
iter: 6 , chi= 775.221496 , Lambda= 43895.747599
iter: 7 , chi= 773.612238 , Lambda= 29263.831733
iter: 8 , chi= 771.890227 , Lambda= 19509.221155
iter: 9 , chi= 770.232471 , Lambda= 13006.147437
problem solve cost: 204.635000 ms
   makeHessian cost: 160.102000 ms
----------- update bprior -------------
             before: 32990.497548
                     16.372910
             after: 36348.958273
                    16.900114
edge factor cnt: 0
1 BackEnd processImage dt: 221.618000 stamp: 1403638523.527829 p_wi: -0.002942 -0.034283  0.004429
iter: 0 , chi= 801.218070 , Lambda= 500000.000000
iter: 1 , chi= 798.604132 , Lambda= 333333.333333
iter: 2 , chi= 797.858588 , Lambda= 222222.222222
iter: 3 , chi= 796.842739 , Lambda= 148148.148148
iter: 4 , chi= 796.007481 , Lambda= 98765.432099
iter: 5 , chi= 795.168622 , Lambda= 65843.621399
iter: 6 , chi= 794.910050 , Lambda= 43895.747599
iter: 7 , chi= 794.839043 , Lambda= 29263.831733
iter: 8 , chi= 794.757570 , Lambda= 19509.221155
iter: 9 , chi= 794.655636 , Lambda= 13006.147437
problem solve cost: 194.144000 ms
   makeHessian cost: 151.773000 ms
----------- update bprior -------------
             before: 36218.304054
                     16.132964
             after: 35851.569961
                    16.247883
edge factor cnt: 467
1 BackEnd processImage dt: 230.390000 stamp: 1403638523.627830 p_wi: -0.013165 -0.042818 -0.072382
iter: 0 , chi= 1069.533261 , Lambda= 500000.000000
iter: 1 , chi= 1053.418973 , Lambda= 333333.333333
iter: 2 , chi= 1048.820284 , Lambda= 222222.222222
iter: 3 , chi= 1045.711340 , Lambda= 148148.148148
iter: 4 , chi= 1043.434903 , Lambda= 98765.432099
iter: 5 , chi= 1039.571154 , Lambda= 65843.621399
iter: 6 , chi= 1037.512597 , Lambda= 43895.747599
iter: 7 , chi= 1035.312532 , Lambda= 29263.831733
iter: 8 , chi= 1032.699930 , Lambda= 19509.221155
iter: 9 , chi= 1029.609448 , Lambda= 13006.147437
problem solve cost: 196.057000 ms
   makeHessian cost: 155.347000 ms
----------- update bprior -------------
             before: 33413.110198
                     20.197264
             after: 39000.148790
                    20.790668
edge factor cnt: 591
1 BackEnd processImage dt: 246.134000 stamp: 1403638523.727829 p_wi: -0.025514 -0.049371 -0.132863
iter: 0 , chi= 1310.138242 , Lambda= 500000.000000
iter: 1 , chi= 1290.254593 , Lambda= 333333.333333
iter: 2 , chi= 1286.818376 , Lambda= 222222.222222
iter: 3 , chi= 1283.928965 , Lambda= 148148.148148
iter: 4 , chi= 1280.561228 , Lambda= 98765.432099
iter: 5 , chi= 1276.329520 , Lambda= 65843.621399
iter: 6 , chi= 1270.692393 , Lambda= 43895.747599
iter: 7 , chi= 1262.817790 , Lambda= 29263.831733
iter: 8 , chi= 1254.598696 , Lambda= 19509.221155
iter: 9 , chi= 1246.756114 , Lambda= 13006.147437
problem solve cost: 198.699000 ms
   makeHessian cost: 160.930000 ms
----------- update bprior -------------
             before: 46838.490385
                     25.318841
             after: 54468.008983
                    25.717354
edge factor cnt: 730
1 BackEnd processImage dt: 241.043000 stamp: 1403638523.827830 p_wi: -0.041008 -0.054122 -0.179356
iter: 0 , chi= 1550.392285 , Lambda= 500000.000000
iter: 1 , chi= 1502.995983 , Lambda= 333333.333333
iter: 2 , chi= 1484.334354 , Lambda= 222222.222222
iter: 3 , chi= 1467.606615 , Lambda= 148148.148148
iter: 4 , chi= 1451.448693 , Lambda= 98765.432099
iter: 5 , chi= 1434.425435 , Lambda= 65843.621399
iter: 6 , chi= 1415.721837 , Lambda= 43895.747599
iter: 7 , chi= 1399.357186 , Lambda= 29263.831733
iter: 8 , chi= 1384.925155 , Lambda= 19509.221155
iter: 9 , chi= 1375.210939 , Lambda= 13006.147437
problem solve cost: 187.321000 ms
   makeHessian cost: 148.856000 ms
----------- update bprior -------------
             before: 59873.503809
                     31.473237
             after: 68567.282949
                    31.286783
edge factor cnt: 790
1 BackEnd processImage dt: 228.961000 stamp: 1403638523.927830 p_wi: -0.058104 -0.062289 -0.214709
iter: 0 , chi= 1603.453001 , Lambda= 500000.000000
iter: 1 , chi= 1525.481130 , Lambda= 333333.333333
iter: 2 , chi= 1470.810206 , Lambda= 222222.222222
iter: 3 , chi= 1405.662624 , Lambda= 148148.148148
iter: 4 , chi= 1363.673877 , Lambda= 98765.432099
iter: 5 , chi= 1343.250289 , Lambda= 65843.621399
iter: 6 , chi= 1332.327246 , Lambda= 43895.747599
iter: 7 , chi= 1326.844510 , Lambda= 29263.831733
iter: 8 , chi= 1322.182442 , Lambda= 19509.221155
iter: 9 , chi= 1316.557399 , Lambda= 13006.147437
problem solve cost: 216.895000 ms
   makeHessian cost: 172.423000 ms
----------- update bprior -------------
             before: 87490.668901
                     37.334423
             after: 80645.319205
                    36.450623
edge factor cnt: 838
1 BackEnd processImage dt: 269.576000 stamp: 1403638524.027829 p_wi: -0.067759 -0.069016 -0.244528
iter: 0 , chi= 1434.323578 , Lambda= 500000.000000
iter: 1 , chi= 1391.105990 , Lambda= 333333.333333
iter: 2 , chi= 1377.726917 , Lambda= 222222.222222
iter: 3 , chi= 1367.367963 , Lambda= 148148.148148
iter: 4 , chi= 1358.269425 , Lambda= 98765.432099
iter: 5 , chi= 1350.909867 , Lambda= 65843.621399
iter: 6 , chi= 1347.816204 , Lambda= 43895.747599
iter: 7 , chi= 1345.395316 , Lambda= 29263.831733
iter: 8 , chi= 1342.559157 , Lambda= 19509.221155
iter: 9 , chi= 1339.455141 , Lambda= 13006.147437
problem solve cost: 196.125000 ms
   makeHessian cost: 154.938000 ms
----------- update bprior -------------
             before: 114059.941258
                     40.964931
             after: 99023.065193
                    40.194007
edge factor cnt: 837
1 BackEnd processImage dt: 242.571000 stamp: 1403638524.127830 p_wi: -0.069669 -0.075533 -0.254451
iter: 0 , chi= 1536.097765 , Lambda= 500000.000000
iter: 1 , chi= 1494.664579 , Lambda= 333333.333333
iter: 2 , chi= 1490.029223 , Lambda= 222222.222222
iter: 3 , chi= 1484.799843 , Lambda= 148148.148148
iter: 4 , chi= 1482.651273 , Lambda= 98765.432099
iter: 5 , chi= 1480.168687 , Lambda= 65843.621399
iter: 6 , chi= 1479.650408 , Lambda= 43895.747599
iter: 7 , chi= 1475.177242 , Lambda= 29263.831733
iter: 8 , chi= 1471.207998 , Lambda= 19509.221155
iter: 9 , chi= 1468.554312 , Lambda= 13006.147437
problem solve cost: 200.785000 ms
   makeHessian cost: 157.558000 ms
----------- update bprior -------------
             before: 127069.810658
                     43.272132
             after: 115763.106860
                    42.922662
edge factor cnt: 790
1 BackEnd processImage dt: 247.849000 stamp: 1403638524.227829 p_wi: -0.068546 -0.080281 -0.236345
iter: 0 , chi= 1709.258689 , Lambda= 500000.000000
iter: 1 , chi= 1615.207860 , Lambda= 333333.333333
iter: 2 , chi= 1609.037681 , Lambda= 222222.222222
iter: 3 , chi= 1603.147548 , Lambda= 148148.148148
iter: 4 , chi= 1601.114388 , Lambda= 98765.432099
iter: 5 , chi= 1600.313477 , Lambda= 65843.621399
iter: 6 , chi= 1600.199016 , Lambda= 43895.747599
iter: 7 , chi= 1597.650772 , Lambda= 29263.831733
iter: 8 , chi= 1595.044048 , Lambda= 19509.221155
iter: 9 , chi= 1592.498781 , Lambda= 13006.147437
problem solve cost: 215.490000 ms
   makeHessian cost: 166.501000 ms
----------- update bprior -------------
             before: 113781.819664
                     44.155281
             after: 115444.634657
                    44.264920
edge factor cnt: 753
1 BackEnd processImage dt: 257.131000 stamp: 1403638524.327830 p_wi: -0.067473 -0.080223 -0.197262
iter: 0 , chi= 1440.931473 , Lambda= 500000.000000
iter: 1 , chi= 1425.241460 , Lambda= 333333.333333
iter: 2 , chi= 1422.119414 , Lambda= 222222.222222
iter: 3 , chi= 1420.777400 , Lambda= 148148.148148
iter: 4 , chi= 1419.684935 , Lambda= 98765.432099
iter: 5 , chi= 1418.347581 , Lambda= 65843.621399
iter: 6 , chi= 1416.455510 , Lambda= 43895.747599
iter: 7 , chi= 1413.859472 , Lambda= 29263.831733
iter: 8 , chi= 1410.346409 , Lambda= 19509.221155
sqrt(currentChi_) <= stopThresholdLM_
problem solve cost: 196.886000 ms
   makeHessian cost: 127.699000 ms
----------- update bprior -------------
             before: 69770.568497
                     38.881166
             after: 72689.998650
                    38.788844
edge factor cnt: 689
1 BackEnd processImage dt: 237.178000 stamp: 1403638524.427830 p_wi: -0.067647 -0.079907 -0.152648
iter: 0 , chi= 1231.593173 , Lambda= 500000.000000
iter: 1 , chi= 1231.593173 , Lambda= 18014398509481986097152.000000
sqrt(currentChi_) <= stopThresholdLM_
problem solve cost: 83.356000 ms
   makeHessian cost: 15.171000 ms
----------- update bprior -------------
             before: 46436.255845
                     31.919516
             after: 46436.255845
                    31.919516
edge factor cnt: 641
1 BackEnd processImage dt: 119.542000 stamp: 1403638524.527829 p_wi: -0.070737 -0.080112 -0.112700
iter: 0 , chi= 1288.517295 , Lambda= 500000.000000
iter: 1 , chi= 1288.517295 , Lambda= 18014398509481986097152.000000
sqrt(currentChi_) <= stopThresholdLM_
problem solve cost: 73.814000 ms
   makeHessian cost: 12.162000 ms
----------- update bprior -------------
             before: 49913.403486
                     31.370271
             after: 49913.403486
                    31.370271
edge factor cnt: 650
1 BackEnd processImage dt: 111.780000 stamp: 1403638524.627830 p_wi: -0.073386 -0.078920 -0.076434
iter: 0 , chi= 1422.796583 , Lambda= 500000.000000
iter: 1 , chi= 1422.796583 , Lambda= 18014398509481986097152.000000
sqrt(currentChi_) <= stopThresholdLM_
problem solve cost: 81.417000 ms
   makeHessian cost: 12.464000 ms
----------- update bprior -------------
             before: 63233.286556
                     32.801510
             after: 63233.286556
                    32.801510
edge factor cnt: 621
1 BackEnd processImage dt: 121.892000 stamp: 1403638524.727829 p_wi: -0.077202 -0.075908 -0.050835
iter: 0 , chi= 1621.623890 , Lambda= 500000.000000
iter: 1 , chi= 1621.623890 , Lambda= 18014398509481986097152.000000
sqrt(currentChi_) <= stopThresholdLM_
problem solve cost: 91.428000 ms
   makeHessian cost: 16.504000 ms
----------- update bprior -------------
             before: 79747.458278
                     35.574162
             after: 79747.458278
                    35.574162
edge factor cnt: 604
1 BackEnd processImage dt: 130.321000 stamp: 1403638524.827830 p_wi: -0.083894 -0.072473 -0.040443
iter: 0 , chi= 1875.906090 , Lambda= 500000.000000
iter: 1 , chi= 1875.906090 , Lambda= 18014398509481986097152.000000
sqrt(currentChi_) <= stopThresholdLM_
problem solve cost: 91.879000 ms
   makeHessian cost: 13.470000 ms
----------- update bprior -------------
             before: 116926.465427
                     39.161414
             after: 116926.465427
                    39.161414
edge factor cnt: 637
1 BackEnd processImage dt: 127.697000 stamp: 1403638524.927830 p_wi: -0.092988 -0.068653 -0.045473
iter: 0 , chi= 2187.102491 , Lambda= 500000.000000
iter: 1 , chi= 2187.102491 , Lambda= 18014398509481986097152.000000
sqrt(currentChi_) <= stopThresholdLM_
problem solve cost: 99.919000 ms
   makeHessian cost: 14.222000 ms
----------- update bprior -------------
             before: 174055.355823
                     43.478496
             after: 174055.355823
                    43.478496
edge factor cnt: 0
1 BackEnd processImage dt: 117.768000 stamp: 1403638525.027829 p_wi: -0.106233 -0.066695 -0.070588
iter: 0 , chi= 2228.768144 , Lambda= 500000.000000
iter: 1 , chi= 2228.768144 , Lambda= 18014398509481986097152.000000
sqrt(currentChi_) <= stopThresholdLM_
problem solve cost: 96.634000 ms
   makeHessian cost: 13.822000 ms
----------- update bprior -------------
             before: 171588.900937
                     43.034115
             after: 171588.900937
                    43.034115
edge factor cnt: 648
1 BackEnd processImage dt: 135.358000 stamp: 1403638525.127830 p_wi: -0.120505 -0.065431 -0.119497
iter: 0 , chi= 2377.880994 , Lambda= 500000.000000
iter: 1 , chi= 2377.880994 , Lambda= 18014398509481986097152.000000
sqrt(currentChi_) <= stopThresholdLM_
problem solve cost: 97.825000 ms
   makeHessian cost: 14.555000 ms
----------- update bprior -------------
             before: 168171.583049
                     44.160296
             after: 168171.583049
                    44.160296
edge factor cnt: 0
1 BackEnd processImage dt: 115.886000 stamp: 1403638525.227829 p_wi: -0.136085 -0.064623 -0.186639
iter: 0 , chi= 2395.283405 , Lambda= 500000.000000
iter: 1 , chi= 2395.283405 , Lambda= 18014398509481986097152.000000
sqrt(currentChi_) <= stopThresholdLM_
problem solve cost: 107.141000 ms
   makeHessian cost: 15.033000 ms
----------- update bprior -------------
             before: 167781.619606
                     43.904490
             after: 167781.619606
                    43.904490
edge factor cnt: 0
1 BackEnd processImage dt: 123.770000 stamp: 1403638525.327830 p_wi: -0.154783 -0.063897 -0.264847
iter: 0 , chi= 2394.974157 , Lambda= 500000.000000
iter: 1 , chi= 2394.974157 , Lambda= 18014398509481986097152.000000
sqrt(currentChi_) <= stopThresholdLM_
problem solve cost: 98.519000 ms
   makeHessian cost: 13.840000 ms
----------- update bprior -------------
             before: 167781.619606
                     43.904490
             after: 167781.619606
                    43.904490
edge factor cnt: 594
1 BackEnd processImage dt: 232.593000 stamp: 1403638525.427830 p_wi: -0.176794 -0.061802 -0.346876
iter: 0 , chi= nan , Lambda= 500000.000000
iter: 1 , chi= nan , Lambda= 18014398509481986097152.000000
iter: 2 , chi= nan , Lambda= 822752278660603116858455895396728899888057129711113907845499648475136.000000
iter: 3 , chi= nan , Lambda= 47634102635436898724379873315377984320228608500171483271481892445135526726758807532347872310828455112538944824267416431834545575140033753564315648.000000
iter: 4 , chi= nan , Lambda= 3495959950985713444632080123730709903643510074804632702858961286653459985776588139814929586748127946306818840941912242619549823910362988237896222302398330053978381405443632220440135622373810791965842646989594435053520339544682443171476172588464958603264.000000
iter: 5 , chi= nan , Lambda= inf
iter: 6 , chi= nan , Lambda= inf
iter: 7 , chi= nan , Lambda= inf
iter: 8 , chi= nan , Lambda= inf
iter: 9 , chi= nan , Lambda= inf
problem solve cost: 405.440000 ms
   makeHessian cost: 13.611000 ms
----------- update bprior -------------
             before: nan
                     nan
             after: nan
                    nan
edge factor cnt: 530
1 BackEnd processImage dt: 440.160000 stamp: 1403638525.527829 p_wi: -0.201462 -0.059313 -0.426397
iter: 0 , chi= nan , Lambda= 500000.000000
iter: 1 , chi= nan , Lambda= 18014398509481986097152.000000
iter: 2 , chi= nan , Lambda= 822752278660603116858455895396728899888057129711113907845499648475136.000000
iter: 3 , chi= nan , Lambda= 47634102635436898724379873315377984320228608500171483271481892445135526726758807532347872310828455112538944824267416431834545575140033753564315648.000000
iter: 4 , chi= nan , Lambda= 3495959950985713444632080123730709903643510074804632702858961286653459985776588139814929586748127946306818840941912242619549823910362988237896222302398330053978381405443632220440135622373810791965842646989594435053520339544682443171476172588464958603264.000000
iter: 5 , chi= nan , Lambda= inf
iter: 6 , chi= nan , Lambda= inf
iter: 7 , chi= nan , Lambda= inf
iter: 8 , chi= nan , Lambda= inf
iter: 9 , chi= nan , Lambda= inf
problem solve cost: 361.049000 ms
   makeHessian cost: 16.291000 ms
----------- update bprior -------------
             before: nan
                     nan
             after: nan
                    nan
edge factor cnt: 561
1 BackEnd processImage dt: 396.373000 stamp: 1403638525.627830 p_wi: -0.226856 -0.058145 -0.499777
1 getMeasurements size: 2 imu sizes: 21 feature_buf size: 0 imu_buf size: 48
iter: 0 , chi= nan , Lambda= 500000.000000
iter: 1 , chi= nan , Lambda= 18014398509481986097152.000000
iter: 2 , chi= nan , Lambda= 822752278660603116858455895396728899888057129711113907845499648475136.000000
iter: 3 , chi= nan , Lambda= 47634102635436898724379873315377984320228608500171483271481892445135526726758807532347872310828455112538944824267416431834545575140033753564315648.000000
iter: 4 , chi= nan , Lambda= 3495959950985713444632080123730709903643510074804632702858961286653459985776588139814929586748127946306818840941912242619549823910362988237896222302398330053978381405443632220440135622373810791965842646989594435053520339544682443171476172588464958603264.000000
iter: 5 , chi= nan , Lambda= inf
iter: 6 , chi= nan , Lambda= inf
iter: 7 , chi= nan , Lambda= inf
iter: 8 , chi= nan , Lambda= inf
iter: 9 , chi= nan , Lambda= inf
problem solve cost: 378.360000 ms
   makeHessian cost: 16.244000 ms
----------- update bprior -------------
             before: nan
                     nan
             after: nan
                    nan
edge factor cnt: 722
1 BackEnd processImage dt: 418.112000 stamp: 1403638525.727829 p_wi: -0.251868 -0.057143 -0.565037
iter: 0 , chi= nan , Lambda= 500000.000000
iter: 1 , chi= nan , Lambda= 18014398509481986097152.000000
iter: 2 , chi= nan , Lambda= 822752278660603116858455895396728899888057129711113907845499648475136.000000
iter: 3 , chi= nan , Lambda= 47634102635436898724379873315377984320228608500171483271481892445135526726758807532347872310828455112538944824267416431834545575140033753564315648.000000
iter: 4 , chi= nan , Lambda= 3495959950985713444632080123730709903643510074804632702858961286653459985776588139814929586748127946306818840941912242619549823910362988237896222302398330053978381405443632220440135622373810791965842646989594435053520339544682443171476172588464958603264.000000
iter: 5 , chi= nan , Lambda= inf
iter: 6 , chi= nan , Lambda= inf
iter: 7 , chi= nan , Lambda= inf
iter: 8 , chi= nan , Lambda= inf
iter: 9 , chi= nan , Lambda= inf
problem solve cost: 368.905000 ms
   makeHessian cost: 14.196000 ms
----------- update bprior -------------
             before: nan
                     nan
             after: nan
                    nan
edge factor cnt: 756
1 BackEnd processImage dt: 408.779000 stamp: 1403638525.827830 p_wi: -0.277331 -0.055067 -0.621944
1 getMeasurements size: 4 imu sizes: 21 feature_buf size: 0 imu_buf size: 43
iter: 0 , chi= nan , Lambda= 500000.000000
iter: 1 , chi= nan , Lambda= 18014398509481986097152.000000
iter: 2 , chi= nan , Lambda= 822752278660603116858455895396728899888057129711113907845499648475136.000000
iter: 3 , chi= nan , Lambda= 47634102635436898724379873315377984320228608500171483271481892445135526726758807532347872310828455112538944824267416431834545575140033753564315648.000000
iter: 4 , chi= nan , Lambda= 3495959950985713444632080123730709903643510074804632702858961286653459985776588139814929586748127946306818840941912242619549823910362988237896222302398330053978381405443632220440135622373810791965842646989594435053520339544682443171476172588464958603264.000000
iter: 5 , chi= nan , Lambda= inf
iter: 6 , chi= nan , Lambda= inf
iter: 7 , chi= nan , Lambda= inf
iter: 8 , chi= nan , Lambda= inf
iter: 9 , chi= nan , Lambda= inf
problem solve cost: 386.377000 ms
   makeHessian cost: 14.996000 ms
----------- update bprior -------------
             before: nan
                     nan
             after: nan
                    nan
edge factor cnt: 821
1 BackEnd processImage dt: 431.753000 stamp: 1403638525.927830 p_wi: -0.304992 -0.055925 -0.669915
iter: 0 , chi= nan , Lambda= 500000.000000
iter: 1 , chi= nan , Lambda= 18014398509481986097152.000000
iter: 2 , chi= nan , Lambda= 822752278660603116858455895396728899888057129711113907845499648475136.000000
iter: 3 , chi= nan , Lambda= 47634102635436898724379873315377984320228608500171483271481892445135526726758807532347872310828455112538944824267416431834545575140033753564315648.000000
iter: 4 , chi= nan , Lambda= 3495959950985713444632080123730709903643510074804632702858961286653459985776588139814929586748127946306818840941912242619549823910362988237896222302398330053978381405443632220440135622373810791965842646989594435053520339544682443171476172588464958603264.000000
iter: 5 , chi= nan , Lambda= inf
iter: 6 , chi= nan , Lambda= inf
iter: 7 , chi= nan , Lambda= inf
iter: 8 , chi= nan , Lambda= inf
iter: 9 , chi= nan , Lambda= inf
problem solve cost: 420.134000 ms
   makeHessian cost: 13.650000 ms
----------- update bprior -------------
             before: nan
                     nan
             after: nan
                    nan
edge factor cnt: 785
1 BackEnd processImage dt: 461.705000 stamp: 1403638526.027829 p_wi: -0.335642 -0.066689 -0.708128
iter: 0 , chi= nan , Lambda= 500000.000000
iter: 1 , chi= nan , Lambda= 18014398509481986097152.000000
iter: 2 , chi= nan , Lambda= 822752278660603116858455895396728899888057129711113907845499648475136.000000
iter: 3 , chi= nan , Lambda= 47634102635436898724379873315377984320228608500171483271481892445135526726758807532347872310828455112538944824267416431834545575140033753564315648.000000
iter: 4 , chi= nan , Lambda= 3495959950985713444632080123730709903643510074804632702858961286653459985776588139814929586748127946306818840941912242619549823910362988237896222302398330053978381405443632220440135622373810791965842646989594435053520339544682443171476172588464958603264.000000
iter: 5 , chi= nan , Lambda= inf
iter: 6 , chi= nan , Lambda= inf
iter: 7 , chi= nan , Lambda= inf
iter: 8 , chi= nan , Lambda= inf
iter: 9 , chi= nan , Lambda= inf
problem solve cost: 447.874000 ms
   makeHessian cost: 15.610000 ms
----------- update bprior -------------
             before: nan
                     nan
             after: nan
                    nan
edge factor cnt: 746
1 BackEnd processImage dt: 492.594000 stamp: 1403638526.127830 p_wi: -0.367153 -0.088407 -0.738354
iter: 0 , chi= nan , Lambda= 500000.000000
iter: 1 , chi= nan , Lambda= 18014398509481986097152.000000
iter: 2 , chi= nan , Lambda= 822752278660603116858455895396728899888057129711113907845499648475136.000000
iter: 3 , chi= nan , Lambda= 47634102635436898724379873315377984320228608500171483271481892445135526726758807532347872310828455112538944824267416431834545575140033753564315648.000000
iter: 4 , chi= nan , Lambda= 3495959950985713444632080123730709903643510074804632702858961286653459985776588139814929586748127946306818840941912242619549823910362988237896222302398330053978381405443632220440135622373810791965842646989594435053520339544682443171476172588464958603264.000000
iter: 5 , chi= nan , Lambda= inf
iter: 6 , chi= nan , Lambda= inf
iter: 7 , chi= nan , Lambda= inf
iter: 8 , chi= nan , Lambda= inf
iter: 9 , chi= nan , Lambda= inf
problem solve cost: 471.397000 ms
   makeHessian cost: 15.090000 ms
----------- update bprior -------------
             before: nan
                     nan
             after: nan
                    nan
edge factor cnt: 690
1 BackEnd processImage dt: 515.647000 stamp: 1403638526.227829 p_wi: -0.400756 -0.118308 -0.761284
1 getMeasurements size: 8 imu sizes: 21 feature_buf size: 0 imu_buf size: 52
iter: 0 , chi= nan , Lambda= 500000.000000
iter: 1 , chi= nan , Lambda= 18014398509481986097152.000000
iter: 2 , chi= nan , Lambda= 822752278660603116858455895396728899888057129711113907845499648475136.000000
iter: 3 , chi= nan , Lambda= 47634102635436898724379873315377984320228608500171483271481892445135526726758807532347872310828455112538944824267416431834545575140033753564315648.000000
iter: 4 , chi= nan , Lambda= 3495959950985713444632080123730709903643510074804632702858961286653459985776588139814929586748127946306818840941912242619549823910362988237896222302398330053978381405443632220440135622373810791965842646989594435053520339544682443171476172588464958603264.000000
iter: 5 , chi= nan , Lambda= inf
iter: 6 , chi= nan , Lambda= inf
iter: 7 , chi= nan , Lambda= inf
iter: 8 , chi= nan , Lambda= inf
iter: 9 , chi= nan , Lambda= inf
problem solve cost: 444.026000 ms
   makeHessian cost: 15.813000 ms
----------- update bprior -------------
             before: nan
                     nan
             after: nan
                    nan
edge factor cnt: 649
1 BackEnd processImage dt: 481.907000 stamp: 1403638526.327830 p_wi: -0.437289 -0.153459 -0.781337
iter: 0 , chi= nan , Lambda= 500000.000000
iter: 1 , chi= nan , Lambda= 18014398509481986097152.000000
iter: 2 , chi= nan , Lambda= 822752278660603116858455895396728899888057129711113907845499648475136.000000
iter: 3 , chi= nan , Lambda= 47634102635436898724379873315377984320228608500171483271481892445135526726758807532347872310828455112538944824267416431834545575140033753564315648.000000
iter: 4 , chi= nan , Lambda= 3495959950985713444632080123730709903643510074804632702858961286653459985776588139814929586748127946306818840941912242619549823910362988237896222302398330053978381405443632220440135622373810791965842646989594435053520339544682443171476172588464958603264.000000
iter: 5 , chi= nan , Lambda= inf
iter: 6 , chi= nan , Lambda= inf
iter: 7 , chi= nan , Lambda= inf
iter: 8 , chi= nan , Lambda= inf
iter: 9 , chi= nan , Lambda= inf
problem solve cost: 419.517000 ms
   makeHessian cost: 15.422000 ms
----------- update bprior -------------
             before: nan
                     nan
             after: nan
                    nan
edge factor cnt: 661
1 BackEnd processImage dt: 457.779000 stamp: 1403638526.427830 p_wi: -0.477645 -0.193317 -0.802495
iter: 0 , chi= nan , Lambda= 500000.000000
iter: 1 , chi= nan , Lambda= 18014398509481986097152.000000
iter: 2 , chi= nan , Lambda= 822752278660603116858455895396728899888057129711113907845499648475136.000000
iter: 3 , chi= nan , Lambda= 47634102635436898724379873315377984320228608500171483271481892445135526726758807532347872310828455112538944824267416431834545575140033753564315648.000000
iter: 4 , chi= nan , Lambda= 3495959950985713444632080123730709903643510074804632702858961286653459985776588139814929586748127946306818840941912242619549823910362988237896222302398330053978381405443632220440135622373810791965842646989594435053520339544682443171476172588464958603264.000000
iter: 5 , chi= nan , Lambda= inf
iter: 6 , chi= nan , Lambda= inf
iter: 7 , chi= nan , Lambda= inf
iter: 8 , chi= nan , Lambda= inf
iter: 9 , chi= nan , Lambda= inf
problem solve cost: 383.488000 ms
   makeHessian cost: 15.482000 ms
----------- update bprior -------------
             before: nan
                     nan
             after: nan
                    nan
edge factor cnt: 668
1 BackEnd processImage dt: 421.356000 stamp: 1403638526.527829 p_wi: -0.520874 -0.237832 -0.827581
iter: 0 , chi= nan , Lambda= 500000.000000
iter: 1 , chi= nan , Lambda= 18014398509481986097152.000000
iter: 2 , chi= nan , Lambda= 822752278660603116858455895396728899888057129711113907845499648475136.000000
iter: 3 , chi= nan , Lambda= 47634102635436898724379873315377984320228608500171483271481892445135526726758807532347872310828455112538944824267416431834545575140033753564315648.000000
iter: 4 , chi= nan , Lambda= 3495959950985713444632080123730709903643510074804632702858961286653459985776588139814929586748127946306818840941912242619549823910362988237896222302398330053978381405443632220440135622373810791965842646989594435053520339544682443171476172588464958603264.000000
iter: 5 , chi= nan , Lambda= inf
iter: 6 , chi= nan , Lambda= inf
iter: 7 , chi= nan , Lambda= inf
2019-11-16 00:19:47.426694+0800 run_euroc[5569:2277701] GetDYLDEntryPointWithImage(/System/Library/Frameworks/AppKit.framework/Versions/Current/AppKit,_NSCreateAppKitServicesMenu) failed.
iter: 8 , chi= nan , Lambda= inf
iter: 9 , chi= nan , Lambda= inf
problem solve cost: 390.331000 ms
   makeHessian cost: 14.197000 ms
----------- update bprior -------------
             before: nan
                     nan
             after: nan
                    nan

Process finished with exit code 9

backend 中的.h 文件去哪了

请问编译过程中EIGEN库的sparsecore报错是什么原因呢，是跟opencv的版本有关吗

我的opencv版本是3.4.12，编译时部分报错如下：
/usr/include/eigen3/Eigen/src/SparseCore/SparseUtil.h:78:36: error: ‘Sparse’ was not declared in this scope
template struct eval<T,Sparse>
^~~~~~
/usr/include/eigen3/Eigen/src/SparseCore/SparseUtil.h:78:36: note: suggested alternative: ‘IsSparse’
template struct eval<T,Sparse>
^~~~~~
IsSparse
/usr/include/eigen3/Eigen/src/SparseCore/SparseUtil.h:78:42: error: template argument 2 is invalid
template struct eval<T,Sparse>
^
/usr/include/eigen3/Eigen/src/SparseCore/SparseUtil.h:111:49: error: ‘Sparse’ was not declared in this scope
template struct plain_matrix_type<T,Sparse>
^~~~~~
/usr/include/eigen3/Eigen/src/SparseCore/SparseUtil.h:111:49: note: suggested alternative: ‘IsSparse’
template struct plain_matrix_type<T,Sparse>
^~~~~~
IsSparse
/usr/include/eigen3/Eigen/src/SparseCore/SparseUtil.h:111:55: error: template argument 2 is invalid
template struct plain_matrix_type<T,Sparse>
^
/usr/include/eigen3/Eigen/src/SparseCore/SparseUtil.h:121:8: error: ‘plain_object_eval’ is not a class template
struct plain_object_eval<T,Sparse>
^~~~~~~~~~~~~~~~~
/usr/include/eigen3/Eigen/src/SparseCore/SparseUtil.h:121:28: error: ‘Sparse’ was not declared in this scope
struct plain_object_eval<T,Sparse>

请问跑Euroc的MH_04轨迹跑一半会跑飞，怎么办呀？

优化函数更新b_prior_和err_prior_矩阵问题

后端优化，更新b_prior_和err_prior_时候是减去H_prior_ * delta_x， Jt_prior_inv_ * b_prior_. 在VINS-MONO代码中是加上线性化Jacobian 乘以 状态增量。在https://blog.csdn.net/weixin_41394379/article/details/89975386中作者推导的也是加号。
​

global sfm failed

n您好，我想请教一下这个问题怎么解决
我跑Euroc MH05的时候，会在最后结束的时候崩掉，轨迹没有回到原点，想请教一下是什么原因。

为什么后端资源消耗反而比vins更多呢

为啥在eurocV1_01_easy上运行后面就发散了？

为啥在eurocV1_01_easy上运行后面就发散了？

贺博为什么这个代码，我编译到97%就失败了呢？只有run_euroc，还运行不了

只能运行demo中的MH_05_difficult数据吗

为啥换一个euroc数据就跑不了了, 比如:V2_02_medium

请问opencv是基于哪个版本的

请问编译成功了，在运行的时候eigen库报错是什么原因呢？

请问开始运行之后，报错：
run_euroc ：/usr/local/include/eigen3/Eigen/src/Core/Densestorage.h:109: Eigen::lnternal:plain_arrays<T
,Size,MatrixOrArrayOptions,16>::plain array() [with T=double;int Size=270 int MatrixOrArrayOptions
=0] :假设(internal::UIntPtr(eigen_unaligned_array_assert_workaround_gCC47(array))&(15))==0 && this assertion is explained here:"http://eigen.tuxfamily.org/dox-devel/group__TopicUnalignedArrayAssert html"
****失败。
已放弃(核心已转储)

是什么原因呢？