Obstacle Detection with Stereo Vision for Off-Road Vehicle Navigation

Broggi, Alberto; Caraffi, C.; Fedriga, Rean Isabella; Grisleri, Paolo

doi:10.1109/cvpr.2005.503

Cited by 123 publications

(64 citation statements)

References 7 publications

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“…3 Challenging images with which the original ROI segmentation method proposed in [19] gives poor results. The original images are shown in the first row.…”

Section: Segmentation Of the Region Of Interestmentioning

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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“…3 Challenging images with which the original ROI segmentation method proposed in [19] gives poor results. The original images are shown in the first row.…”

Section: Segmentation Of the Region Of Interestmentioning

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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“…To solve the problem of terrain classification, many researchers rely on stereo cameras for terrain classification (Broggi, Caraffi, Fedriga, & Grisleri, 2005;Kelly & Stentz, 1998;Manduchi et al, 2004). A stereo algorithm finds pixel disparities between two aligned images, calculating a threedimensional (3D) point cloud.…”

Section: Vision-basedmentioning

confidence: 99%

Self‐supervised learning to visually detect terrain surfaces for autonomous robots operating in forested terrain

Zhou

McDaniel

et al. 2012

Journal of Field Robotics

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Autonomous robotic navigation in forested environments is difficult because of the highly variable appearance and geometric properties of the terrain. In most navigation systems, researchers assume a priori knowledge of the terrain appearance properties, geometric properties, or both. In forest environments, vegetation such as trees, shrubs, and bushes has appearance and geometric properties that vary with change of seasons, vegetation age, and vegetation species. In addition, in forested environments the terrain surface is often rough, sloped, and/or covered with a surface layer of grass, vegetation, or snow. The complexity of the forest environment presents difficult challenges for autonomous navigation systems. In this paper, a self-supervised sensing approach is introduced that attempts to robustly identify a drivable terrain surface for robots operating in forested terrain. The sensing system employs both LIDAR and vision sensor data. There are three main stages in the system: feature learning, feature training, and terrain prediction. In the feature learning stage, 3D range points from LIDAR are analyzed to obtain an estimate of the ground surface location. In the feature training stage, the ground surface estimate is used to train a visual classifier to discriminate between ground and nonground regions of the image. In the prediction stage, the ground surface location can be estimated at high frequency solely from vision sensor data. C 2012 Wiley Periodicals, Inc.

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“…Interesting information about urban environments may be obtained from the u-v disparity. For example, in the case of the u-disparity, the perpendicular obstacles in front of the vehicle appear as horizontal lines whose pixels intensity is the height of these obstacles, whereas in the case of the v-disparity, the perpendicular obstacles appear as vertical lines whose pixels intensity is the width of the obstacles [3]. Another interesting feature is that the ground profile ahead the vehicle appears as an oblique line, this feature is very useful because the pitch of the stereo rig in relation to the ground can be measured for each frame [4].…”

Section: Introductionmentioning

confidence: 99%

U-V Disparity Analysis in Urban Environments

Musleh

Escalera

Armingol

2012

Computer Aided Systems Theory – EUROCAST 2011

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Abstract. Traditionally obstacles detection is a great topic in computer vision applied to robotics navigation or advance driver assistance system (ADAS). Although other technologies, such as laser, obtain good results to detect obstacles in different environments, stereo vision has the advantage of providing 3D information, improving the knowledge of the environment. A study of the implementation of the u-v disparity in urban environments is presented in this paper, where several tests have been done in different situations which may be difficult to interpret by using a straightforward analysis of the u-v disparity in order to model the environment.

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Obstacle Detection with Stereo Vision for Off-Road Vehicle Navigation

Cited by 123 publications

References 7 publications

Enhanced fog detection and free-space segmentation for car navigation

Enhanced fog detection and free-space segmentation for car navigation

Self‐supervised learning to visually detect terrain surfaces for autonomous robots operating in forested terrain

U-V Disparity Analysis in Urban Environments

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