Integration of Tracked and Recognized Features for Locally and Globally Robust Structure from Motion

Abstract

We present a novel approach to structure from motion that integrates wide baseline local features with tracked features to rapidly and robustly reconstruct scenes from image sequences. Rather than assume that we can create and maintain a consistent and drift-free reconstructed map over an arbitrarily long sequence, we instead create small, independent submaps generated over short periods of time and attempt to link the submaps together via recognized features. The tracked features provide accurate pose estimates frame to frame, while the recognizable local features stabilize the estimate over larger baselines and provide a context for linking submaps together. As each frame in the submap is inserted, we apply real-time bundle adjustment to maintain a high accuracy for the submaps. Recent advances in feature-based object recognition enable us to efficiently localize and link new submaps into a reconstructed map within a localization and mapping context. Because our recognition system can operate efficiently on many more features than previous systems, our approach easily scales to larger maps. We provide results that show that accurate structure and motion estimates can be produced from a handheld camera under shaky camera motion.