Road Segmentation Supervised by an Extended V-Disparity Algorithm for Autonomous Navigation

Soquet, Nicolas; Didier, A.; Hautiére, Nicolas

doi:10.1109/ivs.2007.4290108

Cited by 83 publications

(56 citation statements)

References 12 publications

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“…As a first step, we propose to use a quite opposite scheme, which only assumes that the road is flat. The distance of a pixel in the image is thus assumed given by (4). Large distances are clipped using a parameter c. The distance d c of a pixel P(i, j) is thus expressed by: where N × M denotes the size of the image.…”

Section: Flat World Restorationmentioning

confidence: 99%

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Enhanced fog detection and free-space segmentation for car navigation

Hautiére

Tarel

Halmaoui³

et al. 2011

Machine Vision and Applications

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Free space detection is a primary task for car navigation. Unfortunately, classical approaches have difficulties in adverse weather conditions, in particular in daytime fog. In this paper, a solution is proposed thanks to a contrast restoration approach on images grabbed by an in-vehicle camera. The proposed method improves the state of the art in several ways. First, the segmentation of the fog region of interest is better segmented thanks to the computation of shortest routes maps. Second, the fog density as well as the position of the horizon line are jointly computed. Then, the method restores the contrast of the road by only assuming that the road is flat and, at the same time, detects the vertical objects. Finally, a segmentation of the connected component in front of the vehicle gives the free space area. An experimental validation was carried out to foresee the effectiveness of the method. Different results are shown on sample images extracted from video sequences acquired from an in-vehicle camera. The proposed method is complementary to existing free space area detection methods relying on color segmentation and stereovision.

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Section: Flat World Restorationmentioning

confidence: 99%

“…area is either detected based on color [1] or texture [2] segmentations, deduced from stereovision based obstacles detection [3] or is a combination of both approaches [4]. However, all these methods have difficulties in foggy weather.…”

mentioning

confidence: 99%

Enhanced fog detection and free-space segmentation for car navigation

Hautiére

Tarel

Halmaoui³

et al. 2011

Machine Vision and Applications

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“…The v-disparity method [2], [9], [10] is generally used for road surface identification, owing to its simplicity and effectiveness. However, we previously reported that this method became inaccurate when the vehicle was moving fast on a curve.…”

Section: Road Surface Extractionmentioning

confidence: 99%

Obstacle Detection Based on Occupancy Grid Maps Using Stereovision System

et al. 2010

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We previously reported on an obstacle detection method using a stereovision system. The system generated disparity images that include three-dimensional spatial information. Using these images, obstacles could be detected, but some false positives were generated. In this paper, we attempt to eliminate this problem and propose a method that generates Occupancy Grid Maps based on measurements from a stereovision system which leads to robust obstacle detection. Furthermore, it is confirmed that high distance accuracy can be achieved by using our method.

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“…Others include Dahlkamp et al [19], who used self-supervised learning on color images to extend roads found by LADAR. In the area of stereo-vision Soquet et al [20] used a stereo-based color segmentation algorithm to determine road segments. Texture has also been used as seen in Zhang and Nagel [21], who explore anisotropic texture features of roads for segmentation.…”

Section: Path Findingmentioning

confidence: 99%

Fast color/texture segmentation for outdoor robots

Blas

Agrawal

Sundaresan

et al. 2008

2008 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-We present a fast integrated approach for online segmentation of images for outdoor robots. A compact color and texture descriptor has been developed to describe local color and texture variations in an image. This descriptor is then used in a two-stage fast clustering framework using K-means to perform online segmentation of natural images. We present results of applying our descriptor for segmenting a synthetic image and compare it against other state-of-the-art descriptors. We also apply our segmentation algorithm to the task of detecting natural paths in outdoor images. The whole system has been demonstrated to work online alongside localization, 3D obstacle detection, and planning.

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Road Segmentation Supervised by an Extended V-Disparity Algorithm for Autonomous Navigation

Cited by 83 publications

References 12 publications

Enhanced fog detection and free-space segmentation for car navigation

Enhanced fog detection and free-space segmentation for car navigation

Obstacle Detection Based on Occupancy Grid Maps Using Stereovision System

Fast color/texture segmentation for outdoor robots

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