Motion-without-structure: Real-time multipose optimization for accurate visual odometry

State of the art visual odometry systems use bundle adjustment (BA) like methods to jointly optimize motion and scene structure. Fusing measurements from multiple time steps and optimizing an error criterion in a batch fashion seems to deliver the most accurate results. However, often the scene structure is of no interest and is a mere auxiliary quantity although it contributes heavily to the complexity of the problem. Herein we propose to use a recently developed incremental motion estimator which delivers relative pose displacements between each two frames within a sliding window inducing a pose graph. Moreover, we introduce a method to learn the uncertainty associated with each of the pose displacements. The pose graph is adjusted by non-linear least squares optimization while incorporating a motion model. Thereby we fuse measurements from multiple time steps much in the same sense as BA does. However, we obviate the need to estimate the scene structure yielding a very efficient estimator: Solving the nonlinear least squares problem by a Gauss-Newton method takes approximately 1ms. We show the effectiveness of our method on simulated and real world data and demonstrate substantial improvements over incremental methods.