Change search
Refine search result
1 - 11 of 11
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Rows per page
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sort
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
Select
The maximal number of hits you can export is 250. When you want to export more records please use the 'Create feeds' function.
  • 1.
    Siddiqui, Rafid
    Blekinge Institute of Technology, Faculty of Computing, Department of Communication Systems.
    On Fundamental Elements of Visual Navigation Systems2014Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Visual navigation is a ubiquitous yet complex task which is performed by many species for the purpose of survival. Although visual navigation is actively being studied within the robotics community, the determination of elemental constituents of a robust visual navigation system remains a challenge. Motion estimation is mistakenly considered as the sole ingredient to make a robust autonomous visual navigation system and therefore efforts are made to improve the accuracy of motion estimations. On the contrary, there are other factors which are as important as motion and whose absence could result in inability to perform seamless visual navigation such as the one exhibited by humans. Therefore, it is needed that a general model for a visual navigation system be devised which would describe it in terms of a set of elemental units. In this regard, a set of visual navigation elements (i.e. spatial memory, motion memory, scene geometry, context and scene semantics) are suggested as building blocks of a visual navigation system in this thesis. A set of methods are proposed which investigate the existence and role of visual navigation elements in a visual navigation system. A quantitative research methodology in the form of a series of systematic experiments is conducted on these methods. The thesis formulates, implements and analyzes the proposed methods in the context of visual navigation elements which are arranged into three major groupings; a) Spatial memory b) Motion Memory c) Manhattan, context and scene semantics. The investigations are carried out on multiple image datasets obtained by robot mounted cameras (2D/3D) moving in different environments. Spatial memory is investigated by evaluation of proposed place recognition methods. The recognized places and inter-place associations are then used to represent a visited set of places in the form of a topological map. Such a representation of places and their spatial associations models the concept of spatial memory. It resembles the humans’ ability of place representation and mapping for large environments (e.g. cities). Motion memory in a visual navigation system is analyzed by a thorough investigation of various motion estimation methods. This leads to proposals of direct motion estimation methods which compute accurate motion estimates by basing the estimation process on dominant surfaces. In everyday world, planar surfaces, especially the ground planes, are ubiquitous. Therefore, motion models are built upon this constraint. Manhattan structure provides geometrical cues which are helpful in solving navigation problems. There are some unique geometric primitives (e.g. planes) which make up an indoor environment. Therefore, a plane detection method is proposed as a result of investigations performed on scene structure. The method uses supervised learning to successfully classify the segmented clusters in 3D point-cloud datasets. In addition to geometry, the context of a scene also plays an important role in robustness of a visual navigation system. The context in which navigation is being performed imposes a set of constraints on objects and sections of the scene. The enforcement of such constraints enables the observer to robustly segment the scene and to classify various objects in the scene. A contextually aware scene segmentation method is proposed which classifies the image of a scene into a set of geometric classes. The geometric classes are sufficient for most of the navigation tasks. However, in order to facilitate the cognitive visual decision making process, the scene ought to be semantically segmented. The semantic of indoor scenes as well as semantic of the outdoor scenes are dealt with separately and separate methods are proposed for visual mapping of environments belonging to each type. An indoor scene consists of a corridor structure which is modeled as a cubic space in order to build a map of the environment. A “flash-n-extend” strategy is proposed which is responsible for controlling the map update frequency. The semantics of the outdoor scenes is also investigated and a scene classification method is proposed. The method employs a Markov Random Field (MRF) based classification framework which generates a set of semantic maps.

  • 2. Siddiqui, Rafid
    et al.
    Havaei, Mohammad
    Khatibi, Siamak
    Lindley, Craig
    PLASE: A novel planar surface extraction method for the autonomous navigation of micro-air vehicle2011Conference paper (Refereed)
    Abstract [en]

    A Planar Surface Extraction (PLASE) method is proposed for the indoor navigation of a micro-air vehicle (MAV). The algorithm finds planar clusters from the unorganized point clouds. This is achieved by implementing a novel approach that first segments the data points into clusters and then each cluster is estimated for its planarity. The method is tested on indoor point cloud data obtained by 3D PrimeSense based sensor. In order to validate the algorithm, a simulated model containing a set of planes has been constructed, with noise injected into the model. The results of the empirical evaluation suggest that the method performs well even in the presence of the noise and non-planar objects, suggesting that the method will be a viable one for use in MAV navigation in the presence of noisy sensor data.

  • 3.
    Siddiqui, Rafid
    et al.
    Blekinge Institute of Technology, School of Computing.
    Havaei, Mohammad
    Siamak, Khatibi
    Blekinge Institute of Technology, School of Computing.
    Lindley, Craig
    Blekinge Institute of Technology, School of Computing.
    A novel plane extraction approach using supervised learning2013In: Machine Vision and Applications, ISSN 0932-8092, E-ISSN 1432-1769, Vol. 24, no 6, p. 1229-1237Article in journal (Refereed)
    Abstract [en]

    This paper presents a novel approach for the classification of planar surfaces in an unorganized point clouds. A feature-based planner surface detection method is proposed which classifies a point cloud data into planar and non-planar points by learning a classification model from an example set of planes. The algorithm performs segmentation of the scene by applying a graph partitioning approach with improved representation of association among graph nodes. The planarity estimation of the points in a scene segment is then achieved by classifying input points as planar points which satisfy planarity constraint imposed by the learned model. The resultant planes have potential application in solving simultaneous localization and mapping problem for navigation of an unmanned-air vehicle. The proposed method is validated on real and synthetic scenes. The real data consist of five datasets recorded by capturing three-dimensional(3D) point clouds when a RGBD camera is moved in five different indoor scenes. A set of synthetic 3D scenes are constructed containing planar and non-planar structures. The synthetic data are contaminated with Gaussian and random structure noise. The results of the empirical evaluation on both the real and the simulated data suggest that the method provides a generalized solution for plane detection even in the presence of the noise and non-planar objects in the scene. Furthermore, a comparative study has been performed between multiple plane extraction methods.

  • 4.
    Siddiqui, Rafid
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Communication Systems.
    Khatibi, Siamak
    Blekinge Institute of Technology, Faculty of Computing, Department of Communication Systems.
    Bio-inspired Metaheuristic based Visual Tracking and Ego-motion Estimation2014Conference paper (Refereed)
    Abstract [en]

    The problem of robust extraction of ego-motion from a sequence of images for an eye-in-hand camera configuration is addressed. A novel approach toward solving planar template based tracking is proposed which performs a non-linear image alignment and a planar similarity optimization to recover camera transformations from planar regions of a scene. The planar region tracking problem as a motion optimization problem is solved by maximizing the similarity among the planar regions of a scene. The optimization process employs an evolutionary metaheuristic approach in order to address the problem within a large non-linear search space. The proposed method is validated on image sequences with real as well as synthetic image datasets and found to be successful in recovering the ego-motion. A comparative analysis of the proposed method with various other state-of-art methods reveals that the algorithm succeeds in tracking the planar regions robustly and is comparable to the state-of-the art methods. Such an application of evolutionary metaheuristic in solving complex visual navigation problems can provide different perspective and could help in improving already available methods.

  • 5.
    Siddiqui, Rafid
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Communication Systems.
    Khatibi, Siamak
    Blekinge Institute of Technology, Faculty of Computing, Department of Communication Systems.
    Robust Place Recognition with an Application to Semantic Topological Mapping2013Conference paper (Refereed)
    Abstract [en]

    The problem of robust and invariant representation of places is being addressed. A place recognition technique is proposed followed by an application to a semantic topological mapping. The proposed technique is evaluated on a robot localization database which consists of a large set of images taken under various weather conditions. The results show that the proposed method can robustly recognize the places and is invariant to geometric transformations, brightness changes and noise. The comparative analysis with the state-of-the-art semantic place description methods show that the method outperforms the competing methods and exhibits better average recognition rates.

  • 6.
    Siddiqui, Rafid
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Communication Systems.
    Khatibi, Siamak
    Blekinge Institute of Technology, Faculty of Computing, Department of Communication Systems.
    Robust Visual Odometry Estimation of Road Vehicle from Dominant Surfaces for Large Scale Mapping2015In: IET Intelligent Transport Systems, ISSN 1751-956X, E-ISSN 1751-9578, Vol. 9, no 3, p. 314-322Article in journal (Refereed)
    Abstract [en]

    Every urban environment contains a rich set of dominant surfaces which can provide a solid foundation for visual odometry estimation. In this work visual odometry is robustly estimated by computing the motion of camera mounted on a vehicle. The proposed method first identifies a planar region and dynamically estimates the plane parameters. The candidate region and estimated plane parameters are then tracked in the subsequent images and an incremental update of the visual odometry is obtained. The proposed method is evaluated on a navigation dataset of stereo images taken by a car mounted camera that is driven in a large urban environment. The consistency and resilience of the method has also been evaluated on an indoor robot dataset. The results suggest that the proposed visual odometry estimation can robustly recover the motion by tracking a dominant planar surface in the Manhattan environment. In addition to motion estimation solution a set of strategies are discussed for mitigating the problematic factors arising from the unpredictable nature of the environment. The analyses of the results as well as dynamic environmental strategies indicate a strong potential of the method for being part of an autonomous or semi-autonomous system.

  • 7.
    Siddiqui, Rafid
    et al.
    Blekinge Institute of Technology, School of Computing.
    Khatibi, Siamak
    Blekinge Institute of Technology, School of Computing.
    Semantic indoor maps2013Conference paper (Refereed)
    Abstract [en]

    The cumbersome process of construction and incremental update of large indoor maps can be simplified by semantic maps. A novel semantic mapping method for indoor environments is proposed which employs a flash-n-extend strategy for constructing and updating the map. At the exposure of every flash event, a 3D snapshot of the environment is taken which is extended until flash event reoccurs. A flash event occurs at a motion state transition of a mobile robot which is detected by the decomposition of motion estimates. The proposed method is evaluated on a set of image sequences and is found to be robust in building indoor maps which are suitable for robust autonomous navigation. The constructed maps provide simplistic representation of the environment which makes it ideal for high-level reasoning tasks.

  • 8.
    Siddiqui, Rafid
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Communication Systems.
    Khatibi, Siamak
    Blekinge Institute of Technology, Faculty of Computing, Department of Communication Systems.
    Semantic Urban Maps2014In: International Conference on Pattern Recognition, IEEE , 2014, p. 4050-4055Conference paper (Refereed)
    Abstract [en]

    A novel region based 3D semantic mapping method is proposed for urban scenes. The proposed Semantic Urban Maps (SUM) method labels the regions of segmented images into a set of geometric and semantic classes simultaneously by employing a Markov Random Field based classification framework. The pixels in the labeled images are back-projected into a set of 3D point-clouds using stereo disparity. The point-clouds are registered together by incorporating the motion estimation and a coherent semantic map representation is obtained. SUM is evaluated on five urban benchmark sequences and is demonstrated to be successful in retrieving both geometric as well as semantic labels. The comparison with relevant state-of-art method reveals that SUM is competitive and performs better than the competing method in average pixel-wise accuracy.

  • 9.
    Siddiqui, Rafid
    et al.
    Blekinge Institute of Technology, School of Computing.
    Khatibi, Siamak
    Blekinge Institute of Technology, School of Computing.
    Bitra, Sridhar
    Blekinge Institute of Technology, School of Computing.
    Tavassoli, Sam
    Blekinge Institute of Technology, School of Computing.
    Scene perception by context-aware dominant surfaces2013Conference paper (Refereed)
    Abstract [en]

    Most of the computer vision algorithms operate pixel-wise and process image in a small neighborhood for feature extraction. Such a feature extraction strategy ignores the context of an object in the real world. Taking geometric context into account while classifying various regions in a scene, we can discriminate the similar features obtained from different regions with respect to their context. A geometric context based scene decomposition method is proposed and is applied in a context-aware Augmented Reality (AR) system. The proposed system segments a single image of a scene into a set of semantic classes representing dominant surfaces in the scene. The classification method is evaluated on an urban driving sequence with labeled ground truths and found to be robust in classifying the scene regions into a set of dominant applicable surfaces. The classified dominant surfaces are used to generate a 3D scene. The generated 3D scene provides an input to the AR system. The visual experience of 3D scene through the contextually aware AR system provides a solution for visual touring from single images as well as an experimental tool for improving the understanding of human visual perception.

  • 10.
    Siddiqui, Rafid
    et al.
    Blekinge Institute of Technology, School of Computing.
    Lindley, Craig
    Blekinge Institute of Technology, School of Computing.
    Multi-Cue Based Place Learning for Mobile Robot Navigation2012Conference paper (Refereed)
    Abstract [en]

    Place recognition is important navigation ability for autonomous navigation of mobile robots. Visual cues extracted from images provide a way to represent and recognize visited places. In this article, a multi-cue based place learning algorithm is proposed. The algorithm has been evaluated on a localization image database containing different variations of scenes under different weather conditions taken by moving the robot-mounted camera in an indoor-environment. The results suggest that joining the features obtained from different cues provide better representation than using a single feature cue.

  • 11.
    Siddiqui, Rafid
    et al.
    Blekinge Institute of Technology, School of Computing.
    Lindley, Craig
    Blekinge Institute of Technology, School of Computing.
    Spatial cognitive mapping for the Navigation of mobile robots2012Conference paper (Refereed)
    Abstract [en]

    Spatial mapping is an important task in autonomous navigation of mobile robots. Rodents solve the navigation problem by using place and head-direction cells which utilize both idiothetic and allothetic information in the surroundings. This article proposes a spatial cognitive mapping model based on the concepts of rodent’s hippocampus cells. The model has been tested on position sensor data collected using a UAV platform.

1 - 11 of 11
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf