  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.
