Propagation of innovative information in non-linear least-squares structure from motion

We present a new technique that improves upon existing structure from motion (SFM) methods. We propose a SFM algorithm that is both recursive and optimal. Our method incorporates innovative information from new frames into an existing solution without optimizing every camera pose and scene structure parameter. To do this, we incrementally optimize larger subsets of parameters until the error is minimized. These additional parameters are included in the optimization by tracing connections between points and frames. In many cases, the complexity of adding a frame is much smaller than full bundle adjustment of all the parameters. Our algorithm is best described us incremental bundle adjustment as it allows new information to be added to art existing non-linear least-squares solution.