Student's t robust bundle adjustment algorithm

Bundle adjustment (BA) is the problem of refining viewing and structure estimates in multi-view scene reconstruction subject to a scene model (e.g. a set of geometric constraints). Mismatched interest points cause serious problems for the standard least squares approach, as a single mismatch (i.e. outlier) will affect the entire reconstruction. We propose a novel robust Student's t BA algorithm (RST-BA), using the heavy tailed t-distribution to model reprojection errors. We design a custom algorithm to find the maximum a posteriori (MAP) estimates of the camera and viewing parameters. The algorithm exploits the same structure as L
₂
-BA, matching the performance of fast L
₂
implementations. RST-BA is more accurate than either L
₂
-BA or L
₂
-BA with a σ-edit outlier removal rule for a range of simulated error generation scenarios. RST-BA also achieved better median reproduction error recovery than SBA [1] or SBA with outlier removal for large publicly available datasets.