Indoor scene terrain modeling using multiple range images for autonomous mobile robots

Nashashibi, Fawzi; Devy, Michel; Fillatreau, Philippe

doi:10.1109/robot.1992.220337

Cited by 22 publications

(9 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Not only are these systems very expensive, but also their setup and configuration difficult to manage [4]. Hence, visual-based techniques have increased their presence in robot navigation systems along the past years due to their simplicity and low cost.…”

Section: Motivationmentioning

confidence: 99%

Image-based floor segmentation in visual inertial navigation

Barcelo

Panahandeh

Jansson

2013

2013 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)

View full text Add to dashboard Cite

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...

show abstract

Section: Motivationmentioning

confidence: 99%

Image-based floor segmentation in visual inertial navigation

Barcelo

Panahandeh

Jansson

2013

2013 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)

View full text Add to dashboard Cite

show abstract

“…The number of operations is higher, but it is not necessary to perform interpolations or to preprocess the sensor information. Among these methods, we highlight the interpolation of four connected points performed by Nashashibi et al [25] and the use of vision masks in a neighborhood of a point [26]. The last method has been chosen to evaluate the slope in each point in this paper.…”

Section: A Traversabilitymentioning

confidence: 99%

Compact Modeling Technique for Outdoor Navigation

Castejón

Blanco

Moreno

2008

IEEE Trans. Syst., Man, Cybern. A

View full text Add to dashboard Cite

Abstract-In this paper, a new methodology to build compact local maps in real time for outdoor robot navigation is presented. The environment information is obtained from a 3-D scanner laser. The navigation model, which is called traversable region model, is based on a Voronoi diagram technique, but adapted to large outdoor environments. The model obtained with this methodology allows a definition of safe trajectories that depend on the robot's capabilities and the terrain properties, and it will represent, in a topogeometric way, the environment as local and global maps. The application presented is validated in real outdoor environments with the robot called GOLIAT.

show abstract

“…Nashashibi and co-workers [84] describe a system for indoor scene terrain modeling using multiple range images. This relies on two grid-based representations: the local elevation map, and the local navigation map.…”

Section: Environment Map Buildingmentioning

confidence: 99%

“…Artificial neural networks form the architecture of systems that use some form of learning, such as those of Baluja and Pomerleau [82], and Thrun [92]. As mentioned in Section 4, Active map-building strategies primarily consider grid-based maps (e.g., Nashashibi et al [84], Elfes [91] (using sonar data)) as against topological maps (e.g., [105] (using sonar and laser range data)). Thrun [95] proposes an approach that integrates both paradigms.…”

Section: Reactive Robot Navigationmentioning

confidence: 99%

Active recognition through next view planning: a survey

Roy

Chaudhury

Banerjee

2004

Pattern Recognition

View full text Add to dashboard Cite

Abstract3-D object recognition involves using image-computable features to identify 3-D object. A single view of a 3-D object may not contain sufficient features to recognize it unambiguously. One needs to plan different views around the given object in order to recognize it. Such a task involves an active sensor -one whose parameters (external and/or internal) can be changed in a purposive manner. In this paper, we review two important applications of an active sensor. We first survey important approaches to active 3-D object recognition. Next, we review existing approaches towards another important application of an active sensor namely, that of scene analysis and interpretation.

show abstract

Indoor scene terrain modeling using multiple range images for autonomous mobile robots

Cited by 22 publications

References 5 publications

Image-based floor segmentation in visual inertial navigation

Image-based floor segmentation in visual inertial navigation

Compact Modeling Technique for Outdoor Navigation

Active recognition through next view planning: a survey

Contact Info

Product

Resources

About