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Semantic Urban Maps
Department of Computing, Blekinge Institute of Technology, Karlskrona, Sweden.ORCID iD: 0000-0003-4692-5415
Department of Computing, Blekinge Institute of Technology, Karlskrona, Sweden.
2014 (English)In: 22nd International Conference on Pattern Recognition: Proceedings, IEEE conference proceedings, 2014, p. 4050-4055Conference paper, Published 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.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2014. p. 4050-4055
Series
International Conference on Pattern Recognition, ISSN 1051-4651
Keywords [en]
semantic classification; semantic mapping; visual navigation
National Category
Signal Processing Computer Sciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:oru:diva-46476DOI: 10.1109/ICPR.2014.694ISI: 000359818004031Scopus ID: 2-s2.0-84919934206ISBN: 978-1-4799-5208-3 (print)OAI: oai:DiVA.org:oru-46476DiVA, id: diva2:869012
Conference
22nd International Conference on Pattern Recognition (ICPR), Stockholm, Sweden, August 24-28, 2014
Available from: 2014-12-17 Created: 2015-11-12 Last updated: 2018-01-10Bibliographically 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. p. 264
Series
Blekinge Institute of Technology Doctoral Dissertation Series, ISSN 1653-2090 ; 13
Keywords
robot navigation, localization, visual mapping, scene understanding, semantic mapping
National Category
Computer Sciences
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: 2018-06-18Bibliographically approved

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Siddiqui, J. Rafid

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Citation style
  • apa
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  • de-DE
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Output format
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