Putting it altogether Use KLT (or SIFT, Harris then correlation to obtain features in [u, v] There are t=1, 2, .. ,r image frames So there are A,1=(R,Ttl, 0 2=[R+2,.,etr=R, Ttr poses There is only one model M= /X, Y, Z I with j=1,.,N features Initialize first guess of model The first guess is a flat model perpendicularto the image and is Zinit away (e. g. Zinit =0.5 meters or any reasonable guess Iterative while err is not small k llllllll/// //for every time frame t, use all N features, run SFM1 once); so SFM1 runs /times here For(t=1;t<=t++) I Inputs: You have f(focal length), M =[X,,Z For each frame t, you have i=1,, N, image feature points and measurements [u, v]it Output: pose 8, After the above is run 0+1=(R, TI1, 0+2=(R T2,.,0(R, Tt r poses are found llllll/ SFM2: model finding/ mmmlmmll For i=1, i<=N i++ (for every feature, use all T frames, run SFM2 once: so SFM2 runs n times here (SFM2: find model phase Pose estimation vo.a Measurement error(Err) small or model and pose stabilizedPutting it altogether • Use KLT (or SIFT, Harris then correlation) to obtain features in [u,v] T • There are t=1,2,…, image frames, • So there are t=1={R,T} t=1 , t=2={R,T} t=2 , …., t=={R,T} t=  poses. • There is only one model Mi=[X,Y,Z]I ,with i=1,..,N features • Initialize first guess of model – The first guess is a flat model perpendicular to the image and is Zinit away (e.g. Zinit = 0.5 meters or any reasonable guess) • Iterative while ( Err is not small ){ – /////////////// SFM1: Pose finding //////////////////////////////////// – //(for every time frame t, use all N features, run SFM1 once); so SFM1 runs  times here – For (t=1; t<=; t++) – { Inputs: You have f(focal length), Mi=[X,Y,Z]i – For each frame t, you have i=1,,,N, image feature points and measurements [u,v] T i,t – Output: pose t – } – After the above is run – t=1={R,T} t=1 , t=2={R,T} t=2 , …., t=={R,T} t=  poses are found – ////////////////////// SFM2: model finding ////////////////////// – (For i=1,i<=N;i++) (for every feature, use all  frames, run SFM2 once; so SFM2 runs N times here) • {SFM2: find model phase} – Measurement error(Err) small or model and pose stabilized} Pose estimation V0.a 19