We propose a linear-time line segment detector that gives accurate results, a controlled number of false detections, and requires no parameter tuning. This algorithm is tested and compared to state-of-the-art algorithms on a wide set of natural images.

LSD: A Fast Line Segment Detector with a False Detection Control

LSD is a linear-time Line Segment Detector giving subpixel accurate results. It is designed to work on any digital image without parameter tuning. It controls its own number of false detections: on average, one false alarm is allowed per image [1]. The method is based on Burns, Hanson, and Riseman’s method [2], and uses an a contrario validation approach according to Desolneux, Moisan, and Morel’s theory [3, 4]. The version described here includes some further

/pdf/lsd-a-line-segment-detector-1bjldt7rdv.pdf

LSD: a Line Segment Detector

EDLines: A real-time line segment detector with a false detection control

Traditional approaches to stereo visual simultaneous localization and mapping (SLAM) rely on point features to estimate the camera trajectory and build a map of the environment. In low-textured environments, though, it is often difficult to find a sufficient number of reliable point features and, as a consequence, the performance of such algorithms degrades. This paper proposes PL-SLAM, a stereo visual SLAM system that combines both points and line segments to work robustly in a wider variety of scenarios, particularly in those where point features are scarce or not well-distributed in the image. PL-SLAM leverages both points and line segments at all the instances of the process: visual odometry, keyframe selection, bundle adjustment, etc. We contribute also with a loop-closure procedure through a novel bag-of-words approach that exploits the combined descriptive power of the two kinds of features. Additionally, the resulting map is richer and more diverse in three-dimensional elements, which can be exploited to infer valuable, high-level scene structures, such as planes, empty spaces, ground plane, etc. (not addressed in this paper). Our proposal has been tested with several popular datasets (such as EuRoC or KITTI), and is compared with state-of-the-art methods such as ORB-SLAM2, revealing a more robust performance in most of the experiments while still running in real time. An open-source version of the PL-SLAM C++ code has been released for the benefit of the community.

/pdf/pl-slam-a-stereo-slam-system-through-the-combination-of-37oaw45ehy.pdf

PL-SLAM: A Stereo SLAM System Through the Combination of Points and Line Segments

We present an improved version of the Simple Linear Iterative Clustering (SLIC) superpixel segmentation. Unlike SLIC, our algorithm is non-iterative, enforces connectivity from the start, requires lesser memory, and is faster. Relying on the superpixel boundaries obtained using our algorithm, we also present a polygonal partitioning algorithm. We demonstrate that our superpixels as well as the polygonal partitioning are superior to the respective state-of-the-art algorithms on quantitative benchmarks.

/pdf/superpixels-and-polygons-using-simple-non-iterative-3gortkfv0y.pdf

Superpixels and Polygons Using Simple Non-iterative Clustering

In this paper we propose a new method for detecting straight line segments in digital images. It improves upon existing methods by giving precise results while controlling the number of false detections and can be applied to any digital image without parameter setting. The method is a nontrivial extension of the approach presented by Desolneux et al. (2000). The core of the method is an algorithm to cut a binary sequences into what we call a multisegment: a set of collinear and disjoint segments. We shall define a functional that measures the so called meaningfulness of a multisegment. This functional allows us to validate detections against an a contrario non-structured model and to select the best ones. The result is a global interpretation, line by line, of the image in terms of straight segments which gives back its geometry with high accuracy. Comparisons with state of the art methods are presented (more examples are available on-line).

http://ieeexplore.ieee.org/iel5/4378863/4379065/04379140.pdf

R.G. von Gioi

Papers

LSD: A Fast Line Segment Detector with a False Detection Control

Multisegment Detection