Edge-based Visual Odometry with Stereo Cameras using Multiple Oriented Quadtrees

We propose an efficient edge-based stereo visual odometry (VO) using multiple quadtrees created according to image gradient orientations. To characterize edges, we classify them into eight orientation groups according to their image gradient directions. Using the edge groups, we construct eight quadtrees and set overlapping areas belonging to adjacent quadtrees for robust and efficient matching. For further acceleration, previously visited tree nodes are stored and reused at the next iteration to warm-start. We propose an edge culling method to extract prominent edgelets and prune redundant edges. The camera motion is estimated by minimizing point-to-edge distances within a re-weighted iterative closest points (ICP) framework, and simultaneously, 3-D structures are recovered by static and temporal stereo settings. To analyze the effects of the proposed methods, we conduct extensive simulations with various settings. Quantitative results on public datasets confirm that our approach has competitive performance with state-of-the-art stereo methods. In addition, we demonstrate the practical values of our system in author-collected modern building scenes with curved edges only.