  Bundle adjustment plays a vital role in feature-based monocular SLAM. In many
modern SLAM pipelines, bundle adjustment is performed to estimate the 6DOF
camera trajectory and 3D map (3D point cloud) from the input feature tracks.
However, two fundamental weaknesses plague SLAM systems based on bundle
adjustment. First, the need to carefully initialise bundle adjustment means
that all variables, in particular the map, must be estimated as accurately as
possible and maintained over time, which makes the overall algorithm
cumbersome. Second, since estimating the 3D structure (which requires
sufficient baseline) is inherent in bundle adjustment, the SLAM algorithm will
encounter difficulties during periods of slow motion or pure rotational motion.
  We propose a different SLAM optimisation core: instead of bundle adjustment,
we conduct rotation averaging to incrementally optimise only camera
orientations. Given the orientations, we estimate the camera positions and 3D
points via a quasi-convex formulation that can be solved efficiently and
globally optimally. Our approach not only obviates the need to estimate and
maintain the positions and 3D map at keyframe rate (which enables simpler SLAM
systems), it is also more capable of handling slow motions or pure rotational
motions.
