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I'm trying to implement a simple pose optimizer with 3 frames. I've calculated 3D points by triangulation. Then, I projected some of these points onto the 3rd frame. I have 3D pts. and projected points. I need to refine the camera pose at third frame. I've already looked through the examples but I couldn't figure out how to optimize it.
Added a vertex as VertexSE3Expmap with a predicted pose for 3rd frame. (Vertex ID 0)
Added all 3d points as VertexSBAPointXYZ. (Vertex IDs 1, 2, ...)
Added an edge for all projected points as EdgeProjectXYZ2UV wtih
set_vertex(0, vertex(i+1)) -> looping with i
set_vertex(1, vertex(0))
set_parameter_id(0, 0)
I'm not sure if this is the right method. It doesn't work for my data. It is like the image below. Green: actual point, red: projected point with predicted pose, magenta: projected point with optimized pose. Can you suggest anything? Thanks!
I'm trying to implement a simple pose optimizer with 3 frames. I've calculated 3D points by triangulation. Then, I projected some of these points onto the 3rd frame. I have 3D pts. and projected points. I need to refine the camera pose at third frame. I've already looked through the examples but I couldn't figure out how to optimize it.
Added a vertex as VertexSE3Expmap with a predicted pose for 3rd frame. (Vertex ID 0)
Added all 3d points as VertexSBAPointXYZ. (Vertex IDs 1, 2, ...)
Added an edge for all projected points as EdgeProjectXYZ2UV wtih
I'm not sure if this is the right method. It doesn't work for my data. It is like the image below. Green: actual point, red: projected point with predicted pose, magenta: projected point with optimized pose. Can you suggest anything? Thanks!
Verbose Output for 10 iter:
iteration= 0 chi2= 29.056211 time= 0.00258552 cumTime= 0.00258552 edges= 1935 schur= 1 lambda= 440.950594 levenbergIter= 1
iteration= 1 chi2= 22.221547 time= 0.00128042 cumTime= 0.00386594 edges= 1935 schur= 1 lambda= 146.983531 levenbergIter= 1
iteration= 2 chi2= 15.924198 time= 0.0012289 cumTime= 0.00509483 edges= 1935 schur= 1 lambda= 48.994510 levenbergIter= 1
iteration= 3 chi2= 10.866332 time= 0.00117517 cumTime= 0.00627001 edges= 1935 schur= 1 lambda= 16.331503 levenbergIter= 1
iteration= 4 chi2= 7.161353 time= 0.00117359 cumTime= 0.00744359 edges= 1935 schur= 1 lambda= 5.443834 levenbergIter= 1
iteration= 5 chi2= 4.878983 time= 0.00118739 cumTime= 0.00863098 edges= 1935 schur= 1 lambda= 1.814611 levenbergIter= 1
iteration= 6 chi2= 3.324435 time= 0.0011277 cumTime= 0.00975869 edges= 1935 schur= 1 lambda= 0.604870 levenbergIter= 1
iteration= 7 chi2= 2.154175 time= 0.00114915 cumTime= 0.0109078 edges= 1935 schur= 1 lambda= 0.201623 levenbergIter= 1
iteration= 8 chi2= 1.328496 time= 0.00118647 cumTime= 0.0120943 edges= 1935 schur= 1 lambda= 0.067208 levenbergIter= 1
iteration= 9 chi2= 0.794137 time= 0.00113145 cumTime= 0.0132258 edges= 1935 schur= 1 lambda= 0.022403 levenbergIter= 1
Total time elapsed: 1.6252547540025262
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