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Robust Place Recognition with an Application to Semantic Topological Mapping
Department of Computing, Blekinge Institute of Technology, Karlskrona, Sweden .ORCID iD: 0000-0003-4692-5415
Department of Computing, Blekinge Institute of Technology, Karlskrona, Sweden .
2013 (English)In: Sixth International Conference on Machine Vision (ICMV 2013) / [ed] Branislav Vuksanovic; Jianhong Zhou; Antanas Verikas, SPIE , 2013Conference paper, Published 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.

Place, publisher, year, edition, pages
SPIE , 2013.
Series
Proceedings of the Society of Photo-Optical Instrumentation Engineers, ISSN 0277-786X ; 9067
Keyword [en]
Landmarks; Place recognition; Topological maps; Visual features; Visual navigation
National Category
Signal Processing Computer Science
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:oru:diva-46477DOI: 10.1117/12.2051490ISI: 000339220200048Scopus ID: 2-s2.0-84901305228ISBN: 978-081949996-7 (print)OAI: oai:DiVA.org:oru-46477DiVA: diva2:869009
Conference
6th International Conference on Machine Vision (ICMV), London, England, November 16-17, 2013
Available from: 2015-05-25 Created: 2015-11-12 Last updated: 2017-10-17Bibliographically approved
In thesis
1. On Fundamental Elements of Visual Navigation Systems
Open this publication in new window or tab >>On Fundamental Elements of Visual Navigation Systems
2014 (English)Doctoral 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.

Place, publisher, year, edition, pages
Karlskrona: Blekinge Institute of Technology, 2014. 264 p.
Series
Blekinge Institute of Technology Doctoral Dissertation Series, ISSN 1653-2090 ; 13
Keyword
robot navigation, localization, visual mapping, scene understanding, semantic mapping
National Category
Computer Science
Identifiers
urn:nbn:se:oru:diva-46484 (URN)978-91-7295-292-8 (ISBN)
Available from: 2015-11-23 Created: 2015-11-12 Last updated: 2017-10-17Bibliographically approved

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Citation style
  • apa
  • ieee
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  • vancouver
  • Other style
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Language
  • de-DE
  • en-GB
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Output format
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