Evidential mapping for mobile robots with range sensors
Mapping for mobile robots integrates noisy spurious sensor data into a single coherent map useful for navigational purposes. There are various frameworks used for mapping, but the Bayesian framework appears to be most popular. In this paper, the theory behind the Bayesian framework as it is used in...
Gespeichert in:
| Veröffentlicht in: | IEEE transactions on instrumentation and measurement 2006-08, Vol.55 (4), p.1422-1429 |
|---|---|
| Hauptverfasser: | , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 1429 |
|---|---|
| container_issue | 4 |
| container_start_page | 1422 |
| container_title | IEEE transactions on instrumentation and measurement |
| container_volume | 55 |
| creator | Tun Yang Aitken, V. |
| description | Mapping for mobile robots integrates noisy spurious sensor data into a single coherent map useful for navigational purposes. There are various frameworks used for mapping, but the Bayesian framework appears to be most popular. In this paper, the theory behind the Bayesian framework as it is used in mapping is briefly compared to a framework based on evidential theory. The remainder of this paper evaluates the use of the evidential framework by simulating its use on a mobile robot with sparse range sensors. A sensor model is described for the range sensors to work with evidential mapping, and the framework was evaluated under varying parameters and in different simulated test environments |
| doi_str_mv | 10.1109/TIM.2006.876399 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_RIE</sourceid><recordid>TN_cdi_crossref_primary_10_1109_TIM_2006_876399</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>1658400</ieee_id><sourcerecordid>2340321451</sourcerecordid><originalsourceid>FETCH-LOGICAL-c320t-5459c4691e93050eacac9dc12f1a8b9180c33afc7f92fd42f8ff54bf58fcc30e3</originalsourceid><addsrcrecordid>eNpdkMFLwzAchYMoOKdnD16KF0_dfkmaNDnK2HQw8TLPIc2SmdE2NekU_3s7Kgie3uV7j8eH0C2GGcYg59v1y4wA8JkoOZXyDE0wY2UuOSfnaAKARS4Lxi_RVUoHACh5UU5Qufz0O9v2XtdZo7vOt_vMhZg1ofK1zWKoQp-yL9-_Z1G3e5sl26YQ0zW6cLpO9uY3p-httdwunvPN69N68bjJDSXQ56xg0hRcYispMLDaaCN3BhOHtagkFmAo1c6UThK3K4gTzrGickw4YyhYOkUP424Xw8fRpl41Phlb17q14ZiUEJIKIiUfyPt_5CEcYzucU4KzwQWhJ2g-QiaGlKJ1qou-0fFbYVAnjWrQqE4a1ahxaNyNDW-t_aM5EwUA_QHTgW3S</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>865155236</pqid></control><display><type>article</type><title>Evidential mapping for mobile robots with range sensors</title><source>IEEE Electronic Library (IEL)</source><creator>Tun Yang ; Aitken, V.</creator><creatorcontrib>Tun Yang ; Aitken, V.</creatorcontrib><description>Mapping for mobile robots integrates noisy spurious sensor data into a single coherent map useful for navigational purposes. There are various frameworks used for mapping, but the Bayesian framework appears to be most popular. In this paper, the theory behind the Bayesian framework as it is used in mapping is briefly compared to a framework based on evidential theory. The remainder of this paper evaluates the use of the evidential framework by simulating its use on a mobile robot with sparse range sensors. A sensor model is described for the range sensors to work with evidential mapping, and the framework was evaluated under varying parameters and in different simulated test environments</description><identifier>ISSN: 0018-9456</identifier><identifier>EISSN: 1557-9662</identifier><identifier>DOI: 10.1109/TIM.2006.876399</identifier><identifier>CODEN: IEIMAO</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Bayesian analysis ; Bayesian methods ; Coherence ; Computational modeling ; Computer simulation ; Fault-tolerant I&M systems ; Humanoid robots ; Instrumentation ; Intelligent sensors ; Mapping ; Mathematical models ; Mobile robots ; Navigation ; Robot sensing systems ; Robotics and automation ; Robots ; Sensor fusion ; Sensor phenomena and characterization ; Sensors ; soft computing for intelligent I&M systems</subject><ispartof>IEEE transactions on instrumentation and measurement, 2006-08, Vol.55 (4), p.1422-1429</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2006</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c320t-5459c4691e93050eacac9dc12f1a8b9180c33afc7f92fd42f8ff54bf58fcc30e3</citedby><cites>FETCH-LOGICAL-c320t-5459c4691e93050eacac9dc12f1a8b9180c33afc7f92fd42f8ff54bf58fcc30e3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/1658400$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,27924,27925,54758</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/1658400$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Tun Yang</creatorcontrib><creatorcontrib>Aitken, V.</creatorcontrib><title>Evidential mapping for mobile robots with range sensors</title><title>IEEE transactions on instrumentation and measurement</title><addtitle>TIM</addtitle><description>Mapping for mobile robots integrates noisy spurious sensor data into a single coherent map useful for navigational purposes. There are various frameworks used for mapping, but the Bayesian framework appears to be most popular. In this paper, the theory behind the Bayesian framework as it is used in mapping is briefly compared to a framework based on evidential theory. The remainder of this paper evaluates the use of the evidential framework by simulating its use on a mobile robot with sparse range sensors. A sensor model is described for the range sensors to work with evidential mapping, and the framework was evaluated under varying parameters and in different simulated test environments</description><subject>Bayesian analysis</subject><subject>Bayesian methods</subject><subject>Coherence</subject><subject>Computational modeling</subject><subject>Computer simulation</subject><subject>Fault-tolerant I&M systems</subject><subject>Humanoid robots</subject><subject>Instrumentation</subject><subject>Intelligent sensors</subject><subject>Mapping</subject><subject>Mathematical models</subject><subject>Mobile robots</subject><subject>Navigation</subject><subject>Robot sensing systems</subject><subject>Robotics and automation</subject><subject>Robots</subject><subject>Sensor fusion</subject><subject>Sensor phenomena and characterization</subject><subject>Sensors</subject><subject>soft computing for intelligent I&M systems</subject><issn>0018-9456</issn><issn>1557-9662</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2006</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpdkMFLwzAchYMoOKdnD16KF0_dfkmaNDnK2HQw8TLPIc2SmdE2NekU_3s7Kgie3uV7j8eH0C2GGcYg59v1y4wA8JkoOZXyDE0wY2UuOSfnaAKARS4Lxi_RVUoHACh5UU5Qufz0O9v2XtdZo7vOt_vMhZg1ofK1zWKoQp-yL9-_Z1G3e5sl26YQ0zW6cLpO9uY3p-httdwunvPN69N68bjJDSXQ56xg0hRcYispMLDaaCN3BhOHtagkFmAo1c6UThK3K4gTzrGickw4YyhYOkUP424Xw8fRpl41Phlb17q14ZiUEJIKIiUfyPt_5CEcYzucU4KzwQWhJ2g-QiaGlKJ1qou-0fFbYVAnjWrQqE4a1ahxaNyNDW-t_aM5EwUA_QHTgW3S</recordid><startdate>20060801</startdate><enddate>20060801</enddate><creator>Tun Yang</creator><creator>Aitken, V.</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>7U5</scope><scope>8FD</scope><scope>L7M</scope><scope>F28</scope><scope>FR3</scope></search><sort><creationdate>20060801</creationdate><title>Evidential mapping for mobile robots with range sensors</title><author>Tun Yang ; Aitken, V.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c320t-5459c4691e93050eacac9dc12f1a8b9180c33afc7f92fd42f8ff54bf58fcc30e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2006</creationdate><topic>Bayesian analysis</topic><topic>Bayesian methods</topic><topic>Coherence</topic><topic>Computational modeling</topic><topic>Computer simulation</topic><topic>Fault-tolerant I&M systems</topic><topic>Humanoid robots</topic><topic>Instrumentation</topic><topic>Intelligent sensors</topic><topic>Mapping</topic><topic>Mathematical models</topic><topic>Mobile robots</topic><topic>Navigation</topic><topic>Robot sensing systems</topic><topic>Robotics and automation</topic><topic>Robots</topic><topic>Sensor fusion</topic><topic>Sensor phenomena and characterization</topic><topic>Sensors</topic><topic>soft computing for intelligent I&M systems</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Tun Yang</creatorcontrib><creatorcontrib>Aitken, V.</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>Technology Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><jtitle>IEEE transactions on instrumentation and measurement</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Tun Yang</au><au>Aitken, V.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Evidential mapping for mobile robots with range sensors</atitle><jtitle>IEEE transactions on instrumentation and measurement</jtitle><stitle>TIM</stitle><date>2006-08-01</date><risdate>2006</risdate><volume>55</volume><issue>4</issue><spage>1422</spage><epage>1429</epage><pages>1422-1429</pages><issn>0018-9456</issn><eissn>1557-9662</eissn><coden>IEIMAO</coden><abstract>Mapping for mobile robots integrates noisy spurious sensor data into a single coherent map useful for navigational purposes. There are various frameworks used for mapping, but the Bayesian framework appears to be most popular. In this paper, the theory behind the Bayesian framework as it is used in mapping is briefly compared to a framework based on evidential theory. The remainder of this paper evaluates the use of the evidential framework by simulating its use on a mobile robot with sparse range sensors. A sensor model is described for the range sensors to work with evidential mapping, and the framework was evaluated under varying parameters and in different simulated test environments</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/TIM.2006.876399</doi><tpages>8</tpages></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 0018-9456 |
| ispartof | IEEE transactions on instrumentation and measurement, 2006-08, Vol.55 (4), p.1422-1429 |
| issn | 0018-9456 1557-9662 |
| language | eng |
| recordid | cdi_crossref_primary_10_1109_TIM_2006_876399 |
| source | IEEE Electronic Library (IEL) |
| subjects | Bayesian analysis Bayesian methods Coherence Computational modeling Computer simulation Fault-tolerant I&M systems Humanoid robots Instrumentation Intelligent sensors Mapping Mathematical models Mobile robots Navigation Robot sensing systems Robotics and automation Robots Sensor fusion Sensor phenomena and characterization Sensors soft computing for intelligent I&M systems |
| title | Evidential mapping for mobile robots with range sensors |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-28T12%3A10%3A55IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_RIE&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Evidential%20mapping%20for%20mobile%20robots%20with%20range%20sensors&rft.jtitle=IEEE%20transactions%20on%20instrumentation%20and%20measurement&rft.au=Tun%20Yang&rft.date=2006-08-01&rft.volume=55&rft.issue=4&rft.spage=1422&rft.epage=1429&rft.pages=1422-1429&rft.issn=0018-9456&rft.eissn=1557-9662&rft.coden=IEIMAO&rft_id=info:doi/10.1109/TIM.2006.876399&rft_dat=%3Cproquest_RIE%3E2340321451%3C/proquest_RIE%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=865155236&rft_id=info:pmid/&rft_ieee_id=1658400&rfr_iscdi=true |