Edge SLAM: Edge Points Based Monocular Visual SLAM

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.

Indoor Dense Depth Map at Drone Hovering

“…We present two indoor datasets suitable for drone navigation to evaluate the performance of our proposed method in comparison with earlier methods. Sparse reconstruction in all the cases are executed using Edge SLAM [1]. Fig.…”

“…In this paper, we present a novel dense depth map estimation using SfSM at drone hovering. Traditional feature based vSLAM approaches [10,11,12,1] produce erroneous metric depth when the baseline is less than ∼8 mm and viewing angle of a 3D point is less than 0.2 • [9]. Despite several works on SfSM [9,13,14,2,3,15,16], there exists inaccuracy in the estimation of dense depth for navigable space detection on the fly.…”

“…In this work, we target to obtain an accurate dense depth map for navigable space detection by a drone in indoor environments using small motion configuration [9]. We use Edge SLAM [1], the most resilient SLAM in low-textured environment, for estimating camera poses and initial sparse 3D points while the drone is in normal motion and allow the drone to hover and continue estimating the small motion camera poses using resectioning [18]. We start the dense depth estimation as soon as we get at least 20 estimated images from Edge SLAM with small motion where the first image in small motion is the reference image.…”

“…Lab-room dataset. Edge SLAM[1] reconstructs the sparse estimation. (a) Sample image and Depth map by[2].…”

Integrating Objects into Monocular SLAM

“…Mostly, all state-of-the-art SLAM systems [12][13][14][15] and reconstruction methods using IMUs [16,17] rely on the pose-graph/ factorgraph optimization [18,19] or bundle adjustment. In the following section we will review the related work on object-SLAM and discuss some limitations in them and the keypoint based approach which motivated for the proposed approach.…”