Unified stereovision for ground, road, and obstacle detection

Lombardi, Paolo; Zanin, Michele; Messelodi, Stefano

doi:10.1109/ivs.2005.1505200

Cited by 45 publications

(38 citation statements)

References 8 publications

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“…A large amount of work has been done during the past years related to obstacle avoidance and ground plane detection (e.g, [11], [12], [13]). Regarding single camera techniques, an interesting approach was proposed by Lorigo et al [14] that uses a combination of color and gradient histograms to distinguish free space from obstacles.…”

Section: Related Workmentioning

confidence: 99%

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Image-based floor segmentation in visual inertial navigation

Barcelo

Panahandeh

Jansson

2013

2013 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)

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Floor segmentation is a challenging problem in image processing. It has a wide range of applications in the engineering field. In mobile robot navigation systems, detecting which pixels belong to the floor is crucial for guiding the robot within an environment, defining the geometry of the scene, or avoiding obstacles.This report presents a floor segmentation algorithm for indoor scenarios that works with single grey-scale images. The portion of the floor closest to the camera is segmented by judiciously joining a set of horizontal and vertical lines, previously detected. Unlike similar methods in the literature, it does not rely on computing the vanishing point and, thus, it adapts faster to changes in camera motion and is not restricted to typical corridor scenes.A second contribution of this thesis project is the moving features detection for points within the segmented floor area. Based on the camera ego-motion, the expected motion of the points on the ground plane is computed and used for rejecting feature points that belong to movable obstacles. A key point of the designed method is its ability to deal with general motion of the camera.The implemented techniques are to be integrated in a visual-aided inertial navigation system (INS) that combines visual and inertial information. This INS requires a certain number of feature point correspondences on the ground plane to correct data from an inertial measurement unit (IMU) and estimate the ego-motion of the camera. Hence, segmenting the floor region and detecting movable features become relevant tasks in order to ensure that the considered features do belong to the ground.iii Sammanfattning Att segmentera golvet är ett utmanande problem i bildbehandling. Det har ett brett tillämpningsområde inom ingenjörsvetenskapen. I navigeringssystem för mobila robotar är detektering av vilka pixlar som tillhör golvet avgörande för att styra roboten i en inomhusmiljö, för att definiera geometrin för scenen, eller för att undvika hinder.Denna rapport presenterar en golvsegmenteringsalgoritm för inomhustillämpningar utifrån enstaka gråskalebilder. Golvytan närmast kameran segmenteras genom att på ett väl underbyggt sätt sammanknyta horisontella och vertikala linjer som tidigare detekterats. Till skillnad från liknande metoder i litteraturen är metoden inte beroende av att skatta den så kallade "vanishing point", därigenom anpassar den sig snabbare till förändringar i kamerans rörelse och är inte begränsad till typiska korridorsscener.Ett ytterligare bidrag i detta examensarbete är en metod för att detektera objektpunkter som rör sig inom den segmenterade golvytan. Baserat på kamerans rörelse, beräknas den förväntade rörelsen hos punkterna på golvet och den används för att förkasta punkter som tillhör rörliga hinder. En viktig egenart hos den designade metoden är dess förmåga att hantera en godtycklig kamerarörelse.De implementerade metoderna ska integreras i ett visuellt tröghetsnavi-geringssystem som kombinerar visuell-och tröghetsinformation. Detta system kräver et...

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Section: Related Workmentioning

confidence: 99%

“…It is normal to assume that no obstacle is placed in the region closest to the camera, since the robot navigation system tends to avoid them. Hence, one can find similar methods in the literature [39] [40]. However, the use of the bounding box presents some problems:…”

Section: Getting Floor Maskmentioning

confidence: 99%

Image-based floor segmentation in visual inertial navigation

Barcelo

Panahandeh

Jansson

2013

2013 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)

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“…We use different ground truth based measures (see [15]) for evaluation (with pixels being True Positives (TP), False Negatives (FN), and False Positives (FP)):…”

Section: Evaluation Of Unmarked Lane Detectionmentioning

confidence: 99%

An Attention-based System Approach for Scene Analysis in Driver Assistance Ein aufmerksamkeitsbasierter Systemansatz zur Szenenanalyse in der Fahrerassistenz

Michalke¹,

Kastner

Adamy

et al. 2008

At - Automatisierungstechnik

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Research on computer vision systems for driver assistance resulted in a variety of isolated approaches mainly performing very specialized tasks like, e. g., lane keeping or traffic sign detection. However, for a full understanding of generic traffic situations, integrated and flexible approaches are needed. We here present a highly integrated vision architecture for an advanced driver assistance system inspired by human cognitive principles. The system uses an attention system as the flexible and generic front-end for all visual processing, allowing a task-specific scene decomposition and search for known objects (based on a short term memory) as well as generic object classes (based on a long term memory). Knowledge fusion, e. g., between an internal 3D representation and a reliable road detection module improves the system performance. The system heavily relies on top-down links to modulate lower processing levels, resulting in a high system robustness.

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“…While some approaches focus on the 3D reconstruction of an entire scene [1], [10] , many others focus on just finding the ground plane [18], [17], [14], [15], [5], [19]. That is, the classification of pixels as either belonging to the ground plane or not.…”

Section: Introductionmentioning

confidence: 99%

Homography-based ground plane detection for mobile robot navigation using a Modified EM algorithm

Conrad

DeSouza

2010

2010 IEEE International Conference on Robotics and Automation

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Abstract-In this paper, a homography-based approach for determining the ground plane using image pairs is presented. Our approach is unique in that it uses a Modified Expectation Maximization algorithm to cluster pixels on images as belonging to one of two possible classes: ground and non-ground pixels. This classification is very useful in mobile robot navigation because, by segmenting out the ground plane, we are left with all possible objects in the scene, which can then be used to implement many mobile robot navigation algorithms such as obstacle avoidance, path planning, target following, landmark detection, etc. Specifically, we demonstrate the usefulness and robustness of our approach by applying it to a target following algorithm. As the results section shows, the proposed algorithm for ground plane detection achieves an almost perfect detection rate (over 99%) despite the relatively higher number of errors in pixel correspondence from the feature matching algorithm used: SIFT.

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Unified stereovision for ground, road, and obstacle detection

Cited by 45 publications

References 8 publications

Image-based floor segmentation in visual inertial navigation

Image-based floor segmentation in visual inertial navigation

An Attention-based System Approach for Scene Analysis in Driver Assistance Ein aufmerksamkeitsbasierter Systemansatz zur Szenenanalyse in der Fahrerassistenz

Homography-based ground plane detection for mobile robot navigation using a Modified EM algorithm

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