Image-based floor segmentation in visual inertial navigation

Abstract

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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“…Barceló et al [9] presented a single grey scale image sequence based indoor navigation, which combined previously detected vertical and horizontal lines together for segmentation. The system detected several types of indoor scenes and its performance was not affected by camera movement.…”

Deep learning semantic segmentation for indoor terrain extraction: Toward better informing free-living wearable gait assessment

“…Design and implementation of floor segmentation algorithms have been implemented within autonomous robots using lightweight methods e.g., CANNY line detection [21] or Superpixels [22]. Whilst their lightweight nature is useful for edge computing devices in ideal environmental conditions, their application within more general (free-living, home) environments may not be advisable given the greater complexity (e.g., clutter on the ground or strong variations in lighting conditions) and the algorithms inability to learn from previous examples.…”