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

In this paper we describe a flexible approach for determining the relative orientation of the camera with respect to the scene. The main premise of the approach is the fact that in man-made environments, the majority of lines is aligned with the principal orthogonal directions of the world coordinate frame. We exploit this observation towards efficient detection and estimation of vanishing points, which provide strong constraints on camera parameters and relative orientation of the camera with respect to the scene. By combining efficient image processing techniques in the line detection and initialization stage we demonstrate that simultaneous grouping and estimation of vanishing directions can be achieved in the absence of internal parameters of the camera. Constraints between vanishing points are then used for partial calibration and relative rotation estimation. The algorithm has been tested in a variety of indoors and outdoors scenes and its efficiency and automation makes it amenable for implementation on robotic platforms. 

Video Compass

Data association is the process of relating features observed in the environment to features viewed previously or to features in a map. This paper presents a graph theoretic method that is applicable to data association problems where the features are observed via a batch process. Batch observations detect a set of features simultaneously or with sufficiently small temporal difference that, with motion compensation, the features can be represented with precise relative coordinates. This data association method is described in the context of two possible navigation applications: metric map building with simultaneous localisation, and topological map based localisation. Experimental results are presented using an indoor mobile robot with a 2D scanning laser sensor. Given two scans from different unknown locations, the features common to both scans are mapped to each other and the relative change in pose (position and orientation) of the vehicle between the two scans is obtained.

/pdf/data-association-for-mobile-robot-navigation-a-graph-3rxne1wa85.pdf

Data association for mobile robot navigation: a graph theoretic approach

There are two basic ways to improve the speed of a low-level vision algorithm: careful code optimization and selective processing. Reducing the computational effort expended on each pixel reduces the time required to process an image by a constant factor. Selective processing on a limited portion of an image using a focus of attention can decrease overall computation by orders of magnitude. A fast pixel-based algorithm is developed that uses these principles to achieve fast extraction of lines for use in vision-guided mobile robot navigation. It builds upon an algorithm for extracting lines by grouping pixels with similar gradient orientation. It allows parametric control of computational resources required to extract lines with particular characteristics. >

A fast line finder for vision-guided robot navigation

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A fast line finder for vision-guided robot navigation