SFM-based positioning（基于建模的视觉定位）
具体方法来自我参与的两篇paper，一篇journal，一篇conference：
PAPER1 : Vision-Based Positioning for Internet-of-Vehicles， IEEE Transactions on Intelligent Transportation Systems, 2016.
PAPER2 : VISION-BASED POSITIONING WITH SUB-METER ACCURACY FOR INTERNET-OF-VEHICLE (CVGIP2015 Best Paper Award)
过程：基于图像的3D建模--点云压缩--3D和2D的匹配
Dataset：http://imlab.tw/positioning/dataset/
注：以下內容是個人筆記，source code目前還沒有release出來。但其中VisualSFM和2D-3D Matching的部份是公開的，可以在他們的主頁找到。
Introduction
images/: put your training images in this folder
testImages/: put your testing images in this folder
bundle/: put the .out file generated by visualSFM in this folder
file_gen/: files generated in the compression step
result/: results generated in the localization step
work_flow_2.m: do the model compression
BatchLocalizer.sh: script to do the localization of the test images
simple_test.m: generate the test result without ground truth
bash_test.m: generate the test result with ground truth
Steps to use the code
- Training Phase
1. Install visualSFM
http://ccwu.me/vsfm/
2. Open VisualSFM
3. Load Images
File->Open + Multi Images->select the training images at ‘Positioning/images/’
4. Feature Matching
Click on ‘Compute Missing Matches’
5. 3D Reconstruction
Click on ‘Compute 3D Reconstruction’
6. Re-order the cameras
Hit ENTER-> ‘sort’->ENTER
7. Save Results
Sfm->Extra Functions->Save Current Model->‘bundle.out’ at ‘Positioning/bundle/’
Save Current Cameras-> ‘list.txt’ at ‘Positioning/’
8. Close VisualSFM
9. Model Compression
Command line -> ./bin/siftb2a list.txt
Execute ‘work_flow_2.m’ in Matlab，record the value of variablepwk.
- Testing Phase
10. Localization
Command line -> ./BatchLocalizer.sh bundle/bundle.out list.txt file_gen/cluster_k_185.txt 100(10^pwk)testImages/(path of testing images)result/(path of results) 100(testing times) 0.4 100
11. Show Testing Result
Execute ‘simple_test.m’ in Matlab, get ‘trainM.mat’(positions of training images), ‘testM.mat’(positions of testing images), ‘point_position.mat’(positions of model points), ’point_color.mat’(colors of model points).
12. Compute The Error With Ground Truth(optional)
Write the positions of the ground truth images into a new matrix ‘trainR.mat’. Then only keep the corresponding rows in ‘trainM’ and delete the others. Write the positions of the testing images into a new matrix ‘testR.mat’.
Execute ‘bash_test.m’ in Matlab, getrefand dev. Variable refstores the position of each test image in the ground truth coordinate.Dev is the error between each test image and the ground truth.