  As an essential part of structure from motion (SfM) and Simultaneous
Localization and Mapping (SLAM) systems, motion averaging has been extensively
studied in the past years and continues to attract surging research attention.
While canonical approaches such as bundle adjustment are predominantly
inherited in most of state-of-the-art SLAM systems to estimate and update the
trajectory in the robot navigation, the practical implementation of bundle
adjustment in SLAM systems is intrinsically limited by the high computational
complexity, unreliable convergence and strict requirements of ideal
initializations. In this paper, we lift these limitations and propose a novel
optimization backbone for visual SLAM systems, where we leverage rotation
averaging to improve the accuracy, efficiency and robustness of conventional
monocular SLAM pipelines. In our approach, we first decouple the rotational and
translational parameters in the camera rigid body transformation and convert
the high-dimensional non-convex nonlinear problem into tractable linear
subproblems in lower dimensions, and show that the subproblems can be solved
independently with proper constraints. We apply the scale parameter with
$l_1$-norm in the pose-graph optimization to address the rotation averaging
robustness against outliers. We further validate the global optimality of our
proposed approach, revisit and address the initialization schemes, pure
rotational scene handling and outlier treatments. We demonstrate that our
approach can exhibit up to 10x faster speed with comparable accuracy against
the state of the art on public benchmarks.
