We describe how planar surfaces can be incorporated into the model update procedure, and are hypothesised from the matched groupings of features between image frames. We present reconstructions of environments taken by an autonomous robot to demonstrate the improvement that can be achieved by this approach. We are able to formulate a bundle adjustment algorithm whose results are independent of both the coordinate frame chosen to represent the scene and the ordering of the images. This method is more efficient that existing approaches to the problem in photogrammetry. The algorithm allows 3D point and line features to be reconstructed.
Implementation is via the efficient recursive partitioning algorithm common in photogrammetry.
Copyrighted Bundle adjustment is a standard photogrammetric technique for optimizing the 3D reconstruction of a scene from multiple images. We address this issue and show that a simple pre-conditioning step removes the effect of the choice of coordinate frame, and together with a set of enforced constraints on the reconstruction, achieves along with this invariance greatly increased convergence speed over existing methods. In this paper we develop the idea for projective reconstruction. In the projective case the approach relies on a currently unproven matrix conjecture, which we nevertheless strongly believe to be correct, based on extensive experimental evidence. Copyrighted For the problem of tracking vehicles on freeways using machine vision, existing systems work well in free-flowing traffic.