  Regardless of the tremendous progress, a truly general purpose pipeline for
Simultaneous Localization and Mapping (SLAM) remains a challenge. We
investigate the reported failure of state of the art (SOTA) SLAM techniques on
egocentric videos. We find that the dominant 3D rotations, low parallax between
successive frames, and primarily forward motion in egocentric videos are the
most common causes of failures. The incremental nature of SOTA SLAM, in the
presence of unreliable pose and 3D estimates in egocentric videos, with no
opportunities for global loop closures, generates drifts and leads to the
eventual failures of such techniques. Taking inspiration from batch mode
Structure from Motion (SFM) techniques, we propose to solve SLAM as an SFM
problem over the sliding temporal windows. This makes the problem well
constrained. Further, we propose to initialize the camera poses using 2D
rotation averaging, followed by translation averaging before structure
estimation using bundle adjustment. This helps in stabilizing the camera poses
when 3D estimates are not reliable. We show that the proposed SLAM technique,
incorporating the two key ideas works successfully for long, shaky egocentric
videos where other SOTA techniques have been reported to fail. Qualitative and
quantitative comparisons on publicly available egocentric video datasets
validate our results.
