Ground plane feature detection in mobile vision-aided inertial navigation

Panahandeh, Ghazaleh; Mohammadiha, Nasser; Jansson, Magnus

doi:10.1109/iros.2012.6385503

Cited by 11 publications

(5 citation statements)

References 20 publications

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“…For key frame extraction, if fast processing is required, method proposed by Raikwar et al [25] is the appropriate one as it requires very less time for extraction of key frames, where as if high accuracy and simplicity is considered then absolute distinction of histograms of successive frames method proposed by Sheena et al [26] comes out to be appropriate one. In ground plane detection phase, to determine ground plane features, kalman filter and outlier rejection approach proposed by Panahandeh et al [33] is concluded to be a good method. To detect obstacles from moving vehicles, novel image registration algorithm VOFOD by Molineros et al [32] is the appropriate one.…”

Section: Results and Conclusionmentioning

confidence: 99%

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Obstacle Detection Techniques in Outdoor Environment: Process, Study and Analysis

Singh¹,

Kaur²

2017

IJIGSP

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Abstract-Obstacle detection is the process in which the upcoming objects in the path are detected and collision with them is avoided by some sort of signalling to the visually impaired person. In this review paper we present a comprehensive and critical survey of Image Processing techniques like vision based, ground plane detection, feature extraction, etc. for detecting the obstacles. Two types of vision based techniques namely (a) Monocular vision based approach (b) Stereo Vision based approach are discussed. Further types of above described approaches are also discussed in the survey. Survey discusses the analysis of the associated work reported in literature in the field of SURF and SIFTS features, monocular vision based approaches, texture features and ground plane obstacle detection.

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Section: Results and Conclusionmentioning

confidence: 99%

“…Algorithm also performed well when ground level in images was less. In 2012, Panahandeh et al [33] figured locomotion by executing an IMU (Inertial Measurement Unit) and ground level in the images was identified in 2 steps. 1 st on the basis of kalman-filter (nonlinear), planer homography was generated.…”

Section: Ground Plane Detectionmentioning

confidence: 99%

Obstacle Detection Techniques in Outdoor Environment: Process, Study and Analysis

Singh¹,

Kaur²

2017

IJIGSP

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“…The ground plane features are selected using a predefined box in the lower part of the image while the outliers are rejected using the filters estimates over time. Detail studies about the ground plane feature detection and outlier removal are given in [31,32]. Time synchronization and temporal ordering between the IMU and the camera measurements are based on methods in [26].…”

Section: Simulation Resultsmentioning

confidence: 99%

Planar-Based Visual Inertial Navigation: Observability Analysis and Motion Estimation

Panahandeh

Hutchinson

Händel

et al. 2015

J Intell Robot Syst

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In this paper, we address the problem of ego-motion estimation by fusing visual and inertial information. The hardware consists of an inertial measurement unit (IMU) and a monocular camera. The camera provides visual observations in the form of features on a horizontal plane. Exploiting the geometric constraint of features on the plane into visual and inertial data, we propose a novel closed form measurement model for this system. Our first contribution in this paper is an observability analysis of the proposed planar-based visual inertial navigation system (VINS). In particular, we prove that the system has only three unobservable states corresponding to global translations parallel to the plane, and rotation around the gravity vector. Hence, compared to general VINS, an advantage of using features on the horizontal plane is that the vertical translation along the normal of the plane becomes observable. As the second contribution, we present a state-space formulation for the pose estimation in the analyzed system and solve it via a modified unscented Kalman filter (UKF). Finally, the findings of the theoretical analysis and 6-DoF motion estimation are validated by simulations as well as using experimental data.

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“…Segmenting the floor and detecting moving objects become significant tasks for guiding the robot within an environment [5], [6]. Specular reflections and textured floors are the main difficulties faced by floor segmentation algorithms [7].…”

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)

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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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Ground plane feature detection in mobile vision-aided inertial navigation

Cited by 11 publications

References 20 publications

Obstacle Detection Techniques in Outdoor Environment: Process, Study and Analysis

Obstacle Detection Techniques in Outdoor Environment: Process, Study and Analysis

Planar-Based Visual Inertial Navigation: Observability Analysis and Motion Estimation

Image-based floor segmentation in visual inertial navigation

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