Robust Monocular SLAM for Egocentric Videos

TL;DR

This paper introduces a robust SLAM method for egocentric videos by reformulating the problem as a batch structure-from-motion task over sliding windows, utilizing rotation and translation averaging to improve stability and accuracy.

Contribution

The authors propose a novel SLAM approach inspired by batch SFM techniques, addressing failures of current methods in egocentric videos through pose initialization and stabilization strategies.

Findings

Successfully handles long, shaky egocentric videos

Outperforms state-of-the-art SLAM techniques on benchmark datasets

Provides both qualitative and quantitative validation

Abstract

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.