# Computing Egomotion with Local Loop Closures for Egocentric Videos

## TL;DR

This paper introduces a robust camera pose estimation method tailored for egocentric videos, leveraging local loop closures and 2D rotation averaging to overcome challenges faced by traditional SLAM algorithms.

## Contribution

The authors propose a novel egocentric-specific approach that uses local loop closures aligned with footsteps and 2D rotation averaging, improving stability over existing methods.

## Key findings

- Enhanced stability in pose estimation on egocentric videos
- Effective use of local loop closures aligned with footsteps
- Outperforms traditional SLAM algorithms on egocentric datasets

## Abstract

Finding the camera pose is an important step in many egocentric video applications. It has been widely reported that, state of the art SLAM algorithms fail on egocentric videos. In this paper, we propose a robust method for camera pose estimation, designed specifically for egocentric videos. In an egocentric video, the camera views the same scene point multiple times as the wearer's head sweeps back and forth. We use this specific motion profile to perform short loop closures aligned with wearer's footsteps. For egocentric videos, depth estimation is usually noisy. In an important departure, we use 2D computations for rotation averaging which do not rely upon depth estimates. The two modification results in much more stable algorithm as is evident from our experiments on various egocentric video datasets for different egocentric applications. The proposed algorithm resolves a long standing problem in egocentric vision and unlocks new usage scenarios for future applications.