Loading…
A Joint Extrinsic Calibration Tool for Radar, Camera and Lidar
We address joint extrinsic calibration of lidar, camera and radar sensors. To simplify calibration, we propose a single calibration target design for all three modalities, and implement our approach in an open-source tool with bindings to Robot Operating System (ROS). Our tool features three optimiz...
Saved in:
Published in: | IEEE transactions on intelligent vehicles 2021-09, Vol.6 (3), p.571-582 |
---|---|
Main Authors: | , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
cited_by | cdi_FETCH-LOGICAL-c333t-4a6c486def1c7705fd22e1566ccf2096d3fa624870cfab5928ad3e6ae19e10bb3 |
---|---|
cites | cdi_FETCH-LOGICAL-c333t-4a6c486def1c7705fd22e1566ccf2096d3fa624870cfab5928ad3e6ae19e10bb3 |
container_end_page | 582 |
container_issue | 3 |
container_start_page | 571 |
container_title | IEEE transactions on intelligent vehicles |
container_volume | 6 |
creator | Domhof, Joris Kooij, Julian F. P. Gavrila, Dariu M. |
description | We address joint extrinsic calibration of lidar, camera and radar sensors. To simplify calibration, we propose a single calibration target design for all three modalities, and implement our approach in an open-source tool with bindings to Robot Operating System (ROS). Our tool features three optimization configurations, namely using error terms for a minimal number of sensor pairs, or using terms for all sensor pairs in combination with loop closure constraints, or by adding terms for structure estimation in a probabilistic model. Apart from relative calibration where relative transformations between sensors are computed, our work also addresses absolute calibration that includes calibration with respect to the mobile robot's body. Two methods are compared to estimate the body reference frame using an external laser scanner, one based on markers and the other based on manual annotation of the laser scan. In the experiments, we evaluate the three configurations for relative calibration . Our results show that using terms for all sensor pairs is most robust, especially for lidar to radar, when minimum five board locations are used. For absolute calibration the median rotation error around the vertical axis reduces from 1^\circ before calibration, to 0.33^\circ using the markers and 0.02^\circ with manual annotations. |
doi_str_mv | 10.1109/TIV.2021.3065208 |
format | article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2565236540</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>9380784</ieee_id><sourcerecordid>2565236540</sourcerecordid><originalsourceid>FETCH-LOGICAL-c333t-4a6c486def1c7705fd22e1566ccf2096d3fa624870cfab5928ad3e6ae19e10bb3</originalsourceid><addsrcrecordid>eNo9kEFLAzEQhYMoWGrvgpeAV7dOkk02uQilVK0UBKleQzabQEq7qckW9N-bUvU0w8x785gPoWsCU0JA3a-XH1MKlEwZCE5BnqERZY2qpIL6_K-XXF6iSc4bACBCUglqhB5m-CWGfsCLryGFPgeL52Yb2mSGEHu8jnGLfUz4zXQm3ZXdziWDTd_hVSiTK3ThzTa7yW8do_fHxXr-XK1en5bz2aqyjLGhqo2wtRSd88Q2DXDfUeoIF8JaT0GJjnkjaC0bsN60XFFpOuaEcUQ5Am3Lxuj2dHef4ufB5UFv4iH1JVJTXl5mgtdQVHBS2RRzTs7rfQo7k741AX0EpQsofQSlf0EVy83JEpxz_3LFJDSyZj-_BWIh</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2565236540</pqid></control><display><type>article</type><title>A Joint Extrinsic Calibration Tool for Radar, Camera and Lidar</title><source>IEEE Electronic Library (IEL) Journals</source><creator>Domhof, Joris ; Kooij, Julian F. P. ; Gavrila, Dariu M.</creator><creatorcontrib>Domhof, Joris ; Kooij, Julian F. P. ; Gavrila, Dariu M.</creatorcontrib><description><![CDATA[We address joint extrinsic calibration of lidar, camera and radar sensors. To simplify calibration, we propose a single calibration target design for all three modalities, and implement our approach in an open-source tool with bindings to Robot Operating System (ROS). Our tool features three optimization configurations, namely using error terms for a minimal number of sensor pairs, or using terms for all sensor pairs in combination with loop closure constraints, or by adding terms for structure estimation in a probabilistic model. Apart from relative calibration where relative transformations between sensors are computed, our work also addresses absolute calibration that includes calibration with respect to the mobile robot's body. Two methods are compared to estimate the body reference frame using an external laser scanner, one based on markers and the other based on manual annotation of the laser scan. In the experiments, we evaluate the three configurations for relative calibration . Our results show that using terms for all sensor pairs is most robust, especially for lidar to radar, when minimum five board locations are used. For absolute calibration the median rotation error around the vertical axis reduces from 1<inline-formula><tex-math notation="LaTeX">^\circ</tex-math></inline-formula> before calibration, to 0.33<inline-formula><tex-math notation="LaTeX">^\circ</tex-math></inline-formula> using the markers and 0.02<inline-formula><tex-math notation="LaTeX">^\circ</tex-math></inline-formula> with manual annotations.]]></description><identifier>ISSN: 2379-8858</identifier><identifier>EISSN: 2379-8904</identifier><identifier>DOI: 10.1109/TIV.2021.3065208</identifier><identifier>CODEN: ITIVBL</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>Annotations ; Calibration ; camera ; Cameras ; Configurations ; intelligent vehicles ; Laser radar ; Lidar ; Markers ; Optimization ; Probabilistic models ; radar ; Robot sensing systems ; Robot vision systems ; Robots ; ROS ; Sensors ; Source code</subject><ispartof>IEEE transactions on intelligent vehicles, 2021-09, Vol.6 (3), p.571-582</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2021</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c333t-4a6c486def1c7705fd22e1566ccf2096d3fa624870cfab5928ad3e6ae19e10bb3</citedby><cites>FETCH-LOGICAL-c333t-4a6c486def1c7705fd22e1566ccf2096d3fa624870cfab5928ad3e6ae19e10bb3</cites><orcidid>0000-0003-4956-490X ; 0000-0001-9919-0710 ; 0000-0002-1810-4196</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9380784$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>315,786,790,27957,27958,55147</link.rule.ids></links><search><creatorcontrib>Domhof, Joris</creatorcontrib><creatorcontrib>Kooij, Julian F. P.</creatorcontrib><creatorcontrib>Gavrila, Dariu M.</creatorcontrib><title>A Joint Extrinsic Calibration Tool for Radar, Camera and Lidar</title><title>IEEE transactions on intelligent vehicles</title><addtitle>TIV</addtitle><description><![CDATA[We address joint extrinsic calibration of lidar, camera and radar sensors. To simplify calibration, we propose a single calibration target design for all three modalities, and implement our approach in an open-source tool with bindings to Robot Operating System (ROS). Our tool features three optimization configurations, namely using error terms for a minimal number of sensor pairs, or using terms for all sensor pairs in combination with loop closure constraints, or by adding terms for structure estimation in a probabilistic model. Apart from relative calibration where relative transformations between sensors are computed, our work also addresses absolute calibration that includes calibration with respect to the mobile robot's body. Two methods are compared to estimate the body reference frame using an external laser scanner, one based on markers and the other based on manual annotation of the laser scan. In the experiments, we evaluate the three configurations for relative calibration . Our results show that using terms for all sensor pairs is most robust, especially for lidar to radar, when minimum five board locations are used. For absolute calibration the median rotation error around the vertical axis reduces from 1<inline-formula><tex-math notation="LaTeX">^\circ</tex-math></inline-formula> before calibration, to 0.33<inline-formula><tex-math notation="LaTeX">^\circ</tex-math></inline-formula> using the markers and 0.02<inline-formula><tex-math notation="LaTeX">^\circ</tex-math></inline-formula> with manual annotations.]]></description><subject>Annotations</subject><subject>Calibration</subject><subject>camera</subject><subject>Cameras</subject><subject>Configurations</subject><subject>intelligent vehicles</subject><subject>Laser radar</subject><subject>Lidar</subject><subject>Markers</subject><subject>Optimization</subject><subject>Probabilistic models</subject><subject>radar</subject><subject>Robot sensing systems</subject><subject>Robot vision systems</subject><subject>Robots</subject><subject>ROS</subject><subject>Sensors</subject><subject>Source code</subject><issn>2379-8858</issn><issn>2379-8904</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><recordid>eNo9kEFLAzEQhYMoWGrvgpeAV7dOkk02uQilVK0UBKleQzabQEq7qckW9N-bUvU0w8x785gPoWsCU0JA3a-XH1MKlEwZCE5BnqERZY2qpIL6_K-XXF6iSc4bACBCUglqhB5m-CWGfsCLryGFPgeL52Yb2mSGEHu8jnGLfUz4zXQm3ZXdziWDTd_hVSiTK3ThzTa7yW8do_fHxXr-XK1en5bz2aqyjLGhqo2wtRSd88Q2DXDfUeoIF8JaT0GJjnkjaC0bsN60XFFpOuaEcUQ5Am3Lxuj2dHef4ufB5UFv4iH1JVJTXl5mgtdQVHBS2RRzTs7rfQo7k741AX0EpQsofQSlf0EVy83JEpxz_3LFJDSyZj-_BWIh</recordid><startdate>20210901</startdate><enddate>20210901</enddate><creator>Domhof, Joris</creator><creator>Kooij, Julian F. P.</creator><creator>Gavrila, Dariu M.</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>8FD</scope><scope>L7M</scope><orcidid>https://orcid.org/0000-0003-4956-490X</orcidid><orcidid>https://orcid.org/0000-0001-9919-0710</orcidid><orcidid>https://orcid.org/0000-0002-1810-4196</orcidid></search><sort><creationdate>20210901</creationdate><title>A Joint Extrinsic Calibration Tool for Radar, Camera and Lidar</title><author>Domhof, Joris ; Kooij, Julian F. P. ; Gavrila, Dariu M.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c333t-4a6c486def1c7705fd22e1566ccf2096d3fa624870cfab5928ad3e6ae19e10bb3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Annotations</topic><topic>Calibration</topic><topic>camera</topic><topic>Cameras</topic><topic>Configurations</topic><topic>intelligent vehicles</topic><topic>Laser radar</topic><topic>Lidar</topic><topic>Markers</topic><topic>Optimization</topic><topic>Probabilistic models</topic><topic>radar</topic><topic>Robot sensing systems</topic><topic>Robot vision systems</topic><topic>Robots</topic><topic>ROS</topic><topic>Sensors</topic><topic>Source code</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Domhof, Joris</creatorcontrib><creatorcontrib>Kooij, Julian F. P.</creatorcontrib><creatorcontrib>Gavrila, Dariu M.</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEL</collection><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>IEEE transactions on intelligent vehicles</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Domhof, Joris</au><au>Kooij, Julian F. P.</au><au>Gavrila, Dariu M.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Joint Extrinsic Calibration Tool for Radar, Camera and Lidar</atitle><jtitle>IEEE transactions on intelligent vehicles</jtitle><stitle>TIV</stitle><date>2021-09-01</date><risdate>2021</risdate><volume>6</volume><issue>3</issue><spage>571</spage><epage>582</epage><pages>571-582</pages><issn>2379-8858</issn><eissn>2379-8904</eissn><coden>ITIVBL</coden><abstract><![CDATA[We address joint extrinsic calibration of lidar, camera and radar sensors. To simplify calibration, we propose a single calibration target design for all three modalities, and implement our approach in an open-source tool with bindings to Robot Operating System (ROS). Our tool features three optimization configurations, namely using error terms for a minimal number of sensor pairs, or using terms for all sensor pairs in combination with loop closure constraints, or by adding terms for structure estimation in a probabilistic model. Apart from relative calibration where relative transformations between sensors are computed, our work also addresses absolute calibration that includes calibration with respect to the mobile robot's body. Two methods are compared to estimate the body reference frame using an external laser scanner, one based on markers and the other based on manual annotation of the laser scan. In the experiments, we evaluate the three configurations for relative calibration . Our results show that using terms for all sensor pairs is most robust, especially for lidar to radar, when minimum five board locations are used. For absolute calibration the median rotation error around the vertical axis reduces from 1<inline-formula><tex-math notation="LaTeX">^\circ</tex-math></inline-formula> before calibration, to 0.33<inline-formula><tex-math notation="LaTeX">^\circ</tex-math></inline-formula> using the markers and 0.02<inline-formula><tex-math notation="LaTeX">^\circ</tex-math></inline-formula> with manual annotations.]]></abstract><cop>Piscataway</cop><pub>IEEE</pub><doi>10.1109/TIV.2021.3065208</doi><tpages>12</tpages><orcidid>https://orcid.org/0000-0003-4956-490X</orcidid><orcidid>https://orcid.org/0000-0001-9919-0710</orcidid><orcidid>https://orcid.org/0000-0002-1810-4196</orcidid><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 2379-8858 |
ispartof | IEEE transactions on intelligent vehicles, 2021-09, Vol.6 (3), p.571-582 |
issn | 2379-8858 2379-8904 |
language | eng |
recordid | cdi_proquest_journals_2565236540 |
source | IEEE Electronic Library (IEL) Journals |
subjects | Annotations Calibration camera Cameras Configurations intelligent vehicles Laser radar Lidar Markers Optimization Probabilistic models radar Robot sensing systems Robot vision systems Robots ROS Sensors Source code |
title | A Joint Extrinsic Calibration Tool for Radar, Camera and Lidar |
url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-09-23T06%3A33%3A09IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=A%20Joint%20Extrinsic%20Calibration%20Tool%20for%20Radar,%20Camera%20and%20Lidar&rft.jtitle=IEEE%20transactions%20on%20intelligent%20vehicles&rft.au=Domhof,%20Joris&rft.date=2021-09-01&rft.volume=6&rft.issue=3&rft.spage=571&rft.epage=582&rft.pages=571-582&rft.issn=2379-8858&rft.eissn=2379-8904&rft.coden=ITIVBL&rft_id=info:doi/10.1109/TIV.2021.3065208&rft_dat=%3Cproquest_cross%3E2565236540%3C/proquest_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c333t-4a6c486def1c7705fd22e1566ccf2096d3fa624870cfab5928ad3e6ae19e10bb3%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2565236540&rft_id=info:pmid/&rft_ieee_id=9380784&rfr_iscdi=true |