# Edge SLAM: Edge Points Based Monocular Visual SLAM

## TL;DR

Edge SLAM introduces a monocular visual SLAM method that leverages edge points and geometrical relationships to improve robustness and accuracy in both textured and low-textured environments.

## Contribution

The paper proposes a novel Edge SLAM pipeline that detects and tracks edge points, refines correspondences with geometrical constraints, and employs a local optimization for reliable camera estimation.

## Key findings

- Robust performance in low-textured environments.
- Outperforms existing SLAM methods on popular datasets.
- Reliable camera estimation even with partial reconstructions.

## Abstract

Visual SLAM shows significant progress in recent years due to high attention from vision community but still, challenges remain for low-textured environments. Feature based visual SLAMs do not produce reliable camera and structure estimates due to insufficient features in a low-textured environment. Moreover, existing visual SLAMs produce partial reconstruction when the number of 3D-2D correspondences is insufficient for incremental camera estimation using bundle adjustment. This paper presents Edge SLAM, a feature based monocular visual SLAM which mitigates the above mentioned problems. Our proposed Edge SLAM pipeline detects edge points from images and tracks those using optical flow for point correspondence. We further refine these point correspondences using geometrical relationship among three views. Owing to our edge-point tracking, we use a robust method for two-view initialization for bundle adjustment. Our proposed SLAM also identifies the potential situations where estimating a new camera into the existing reconstruction is becoming unreliable and we adopt a novel method to estimate the new camera reliably using a local optimization technique. We present an extensive evaluation of our proposed SLAM pipeline with most popular open datasets and compare with the state-of-the art. Experimental result indicates that our Edge SLAM is robust and works reliably well for both textured and less-textured environment in comparison to existing state-of-the-art SLAMs.