Evaluation of 3D LiDAR SLAM algorithms based on the KITTI dataset

Abstract Safe autonomous driving is the future trend, and achieving it requires precise and real-time simultaneous localization and mapping (SLAM). Many practitioners are concerned about the performance of LiDAR SLAM algorithms, but there is little research work to evaluate LiDAR SLAM algorithms spe...
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Autor*in:	Wu, Jiayang [verfasserIn] Huang, Shihong Yang, Yanxu Zhang, Bingzhi

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Simultaneous localization and mapping Filtering Graph optimization Factor graph optimization

Anmerkung:	© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Übergeordnetes Werk:	Enthalten in: The journal of supercomputing - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987, 79(2023), 14 vom: 20. Apr., Seite 15760-15772
Übergeordnetes Werk:	volume:79 ; year:2023 ; number:14 ; day:20 ; month:04 ; pages:15760-15772

Links:	Volltext

DOI / URN:	10.1007/s11227-023-05267-3

Katalog-ID:	SPR052740218

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520			\|a Abstract Safe autonomous driving is the future trend, and achieving it requires precise and real-time simultaneous localization and mapping (SLAM). Many practitioners are concerned about the performance of LiDAR SLAM algorithms, but there is little research work to evaluate LiDAR SLAM algorithms specifically. This paper evaluates LeGO-LOAM, SC-LeGO-LOAM, LIO-SAM, SC-LIO-SAM, and FAST-LIO2 utilizing the 05-10 sequences from KITTI dataset. The experimental results show that: firstly, there is no significant difference among the absolute trajectory error of the five SLAM algorithms. Secondly, LeGO-LAOM has the smallest relative positional error among the six sequences. Thirdly, FAST-LIO2 has the best real-time performance. Our experiments are intended to provide a reference for practitioners in selecting SLAM algorithms.
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700	1		\|a Zhang, Bingzhi \|4 aut
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allfields	10.1007/s11227-023-05267-3 doi (DE-627)SPR052740218 (SPR)s11227-023-05267-3-e DE-627 ger DE-627 rakwb eng Wu, Jiayang verfasserin aut Evaluation of 3D LiDAR SLAM algorithms based on the KITTI dataset 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Safe autonomous driving is the future trend, and achieving it requires precise and real-time simultaneous localization and mapping (SLAM). Many practitioners are concerned about the performance of LiDAR SLAM algorithms, but there is little research work to evaluate LiDAR SLAM algorithms specifically. This paper evaluates LeGO-LOAM, SC-LeGO-LOAM, LIO-SAM, SC-LIO-SAM, and FAST-LIO2 utilizing the 05-10 sequences from KITTI dataset. The experimental results show that: firstly, there is no significant difference among the absolute trajectory error of the five SLAM algorithms. Secondly, LeGO-LAOM has the smallest relative positional error among the six sequences. Thirdly, FAST-LIO2 has the best real-time performance. Our experiments are intended to provide a reference for practitioners in selecting SLAM algorithms. Simultaneous localization and mapping (dpeaa)DE-He213 Filtering (dpeaa)DE-He213 Graph optimization (dpeaa)DE-He213 Factor graph optimization (dpeaa)DE-He213 Huang, Shihong aut Yang, Yanxu aut Zhang, Bingzhi aut Enthalten in The journal of supercomputing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 79(2023), 14 vom: 20. Apr., Seite 15760-15772 (DE-627)271350202 (DE-600)1479917-0 1573-0484 nnns volume:79 year:2023 number:14 day:20 month:04 pages:15760-15772 https://dx.doi.org/10.1007/s11227-023-05267-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 79 2023 14 20 04 15760-15772
spelling	10.1007/s11227-023-05267-3 doi (DE-627)SPR052740218 (SPR)s11227-023-05267-3-e DE-627 ger DE-627 rakwb eng Wu, Jiayang verfasserin aut Evaluation of 3D LiDAR SLAM algorithms based on the KITTI dataset 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Safe autonomous driving is the future trend, and achieving it requires precise and real-time simultaneous localization and mapping (SLAM). Many practitioners are concerned about the performance of LiDAR SLAM algorithms, but there is little research work to evaluate LiDAR SLAM algorithms specifically. This paper evaluates LeGO-LOAM, SC-LeGO-LOAM, LIO-SAM, SC-LIO-SAM, and FAST-LIO2 utilizing the 05-10 sequences from KITTI dataset. The experimental results show that: firstly, there is no significant difference among the absolute trajectory error of the five SLAM algorithms. Secondly, LeGO-LAOM has the smallest relative positional error among the six sequences. Thirdly, FAST-LIO2 has the best real-time performance. Our experiments are intended to provide a reference for practitioners in selecting SLAM algorithms. Simultaneous localization and mapping (dpeaa)DE-He213 Filtering (dpeaa)DE-He213 Graph optimization (dpeaa)DE-He213 Factor graph optimization (dpeaa)DE-He213 Huang, Shihong aut Yang, Yanxu aut Zhang, Bingzhi aut Enthalten in The journal of supercomputing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 79(2023), 14 vom: 20. Apr., Seite 15760-15772 (DE-627)271350202 (DE-600)1479917-0 1573-0484 nnns volume:79 year:2023 number:14 day:20 month:04 pages:15760-15772 https://dx.doi.org/10.1007/s11227-023-05267-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 79 2023 14 20 04 15760-15772
allfields_unstemmed	10.1007/s11227-023-05267-3 doi (DE-627)SPR052740218 (SPR)s11227-023-05267-3-e DE-627 ger DE-627 rakwb eng Wu, Jiayang verfasserin aut Evaluation of 3D LiDAR SLAM algorithms based on the KITTI dataset 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Safe autonomous driving is the future trend, and achieving it requires precise and real-time simultaneous localization and mapping (SLAM). Many practitioners are concerned about the performance of LiDAR SLAM algorithms, but there is little research work to evaluate LiDAR SLAM algorithms specifically. This paper evaluates LeGO-LOAM, SC-LeGO-LOAM, LIO-SAM, SC-LIO-SAM, and FAST-LIO2 utilizing the 05-10 sequences from KITTI dataset. The experimental results show that: firstly, there is no significant difference among the absolute trajectory error of the five SLAM algorithms. Secondly, LeGO-LAOM has the smallest relative positional error among the six sequences. Thirdly, FAST-LIO2 has the best real-time performance. Our experiments are intended to provide a reference for practitioners in selecting SLAM algorithms. Simultaneous localization and mapping (dpeaa)DE-He213 Filtering (dpeaa)DE-He213 Graph optimization (dpeaa)DE-He213 Factor graph optimization (dpeaa)DE-He213 Huang, Shihong aut Yang, Yanxu aut Zhang, Bingzhi aut Enthalten in The journal of supercomputing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 79(2023), 14 vom: 20. Apr., Seite 15760-15772 (DE-627)271350202 (DE-600)1479917-0 1573-0484 nnns volume:79 year:2023 number:14 day:20 month:04 pages:15760-15772 https://dx.doi.org/10.1007/s11227-023-05267-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 79 2023 14 20 04 15760-15772
allfieldsGer	10.1007/s11227-023-05267-3 doi (DE-627)SPR052740218 (SPR)s11227-023-05267-3-e DE-627 ger DE-627 rakwb eng Wu, Jiayang verfasserin aut Evaluation of 3D LiDAR SLAM algorithms based on the KITTI dataset 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Safe autonomous driving is the future trend, and achieving it requires precise and real-time simultaneous localization and mapping (SLAM). Many practitioners are concerned about the performance of LiDAR SLAM algorithms, but there is little research work to evaluate LiDAR SLAM algorithms specifically. This paper evaluates LeGO-LOAM, SC-LeGO-LOAM, LIO-SAM, SC-LIO-SAM, and FAST-LIO2 utilizing the 05-10 sequences from KITTI dataset. The experimental results show that: firstly, there is no significant difference among the absolute trajectory error of the five SLAM algorithms. Secondly, LeGO-LAOM has the smallest relative positional error among the six sequences. Thirdly, FAST-LIO2 has the best real-time performance. Our experiments are intended to provide a reference for practitioners in selecting SLAM algorithms. Simultaneous localization and mapping (dpeaa)DE-He213 Filtering (dpeaa)DE-He213 Graph optimization (dpeaa)DE-He213 Factor graph optimization (dpeaa)DE-He213 Huang, Shihong aut Yang, Yanxu aut Zhang, Bingzhi aut Enthalten in The journal of supercomputing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 79(2023), 14 vom: 20. Apr., Seite 15760-15772 (DE-627)271350202 (DE-600)1479917-0 1573-0484 nnns volume:79 year:2023 number:14 day:20 month:04 pages:15760-15772 https://dx.doi.org/10.1007/s11227-023-05267-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 79 2023 14 20 04 15760-15772
allfieldsSound	10.1007/s11227-023-05267-3 doi (DE-627)SPR052740218 (SPR)s11227-023-05267-3-e DE-627 ger DE-627 rakwb eng Wu, Jiayang verfasserin aut Evaluation of 3D LiDAR SLAM algorithms based on the KITTI dataset 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Safe autonomous driving is the future trend, and achieving it requires precise and real-time simultaneous localization and mapping (SLAM). Many practitioners are concerned about the performance of LiDAR SLAM algorithms, but there is little research work to evaluate LiDAR SLAM algorithms specifically. This paper evaluates LeGO-LOAM, SC-LeGO-LOAM, LIO-SAM, SC-LIO-SAM, and FAST-LIO2 utilizing the 05-10 sequences from KITTI dataset. The experimental results show that: firstly, there is no significant difference among the absolute trajectory error of the five SLAM algorithms. Secondly, LeGO-LAOM has the smallest relative positional error among the six sequences. Thirdly, FAST-LIO2 has the best real-time performance. Our experiments are intended to provide a reference for practitioners in selecting SLAM algorithms. Simultaneous localization and mapping (dpeaa)DE-He213 Filtering (dpeaa)DE-He213 Graph optimization (dpeaa)DE-He213 Factor graph optimization (dpeaa)DE-He213 Huang, Shihong aut Yang, Yanxu aut Zhang, Bingzhi aut Enthalten in The journal of supercomputing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 79(2023), 14 vom: 20. Apr., Seite 15760-15772 (DE-627)271350202 (DE-600)1479917-0 1573-0484 nnns volume:79 year:2023 number:14 day:20 month:04 pages:15760-15772 https://dx.doi.org/10.1007/s11227-023-05267-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 79 2023 14 20 04 15760-15772
language	English
source	Enthalten in The journal of supercomputing 79(2023), 14 vom: 20. Apr., Seite 15760-15772 volume:79 year:2023 number:14 day:20 month:04 pages:15760-15772
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topic_facet	Simultaneous localization and mapping Filtering Graph optimization Factor graph optimization
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authorswithroles_txt_mv	Wu, Jiayang @@aut@@ Huang, Shihong @@aut@@ Yang, Yanxu @@aut@@ Zhang, Bingzhi @@aut@@
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author	Wu, Jiayang
spellingShingle	Wu, Jiayang misc Simultaneous localization and mapping misc Filtering misc Graph optimization misc Factor graph optimization Evaluation of 3D LiDAR SLAM algorithms based on the KITTI dataset
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topic_title	Evaluation of 3D LiDAR SLAM algorithms based on the KITTI dataset Simultaneous localization and mapping (dpeaa)DE-He213 Filtering (dpeaa)DE-He213 Graph optimization (dpeaa)DE-He213 Factor graph optimization (dpeaa)DE-He213
topic	misc Simultaneous localization and mapping misc Filtering misc Graph optimization misc Factor graph optimization
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title	Evaluation of 3D LiDAR SLAM algorithms based on the KITTI dataset
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title_full	Evaluation of 3D LiDAR SLAM algorithms based on the KITTI dataset
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title_sort	evaluation of 3d lidar slam algorithms based on the kitti dataset
title_auth	Evaluation of 3D LiDAR SLAM algorithms based on the KITTI dataset
abstract	Abstract Safe autonomous driving is the future trend, and achieving it requires precise and real-time simultaneous localization and mapping (SLAM). Many practitioners are concerned about the performance of LiDAR SLAM algorithms, but there is little research work to evaluate LiDAR SLAM algorithms specifically. This paper evaluates LeGO-LOAM, SC-LeGO-LOAM, LIO-SAM, SC-LIO-SAM, and FAST-LIO2 utilizing the 05-10 sequences from KITTI dataset. The experimental results show that: firstly, there is no significant difference among the absolute trajectory error of the five SLAM algorithms. Secondly, LeGO-LAOM has the smallest relative positional error among the six sequences. Thirdly, FAST-LIO2 has the best real-time performance. Our experiments are intended to provide a reference for practitioners in selecting SLAM algorithms. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstractGer	Abstract Safe autonomous driving is the future trend, and achieving it requires precise and real-time simultaneous localization and mapping (SLAM). Many practitioners are concerned about the performance of LiDAR SLAM algorithms, but there is little research work to evaluate LiDAR SLAM algorithms specifically. This paper evaluates LeGO-LOAM, SC-LeGO-LOAM, LIO-SAM, SC-LIO-SAM, and FAST-LIO2 utilizing the 05-10 sequences from KITTI dataset. The experimental results show that: firstly, there is no significant difference among the absolute trajectory error of the five SLAM algorithms. Secondly, LeGO-LAOM has the smallest relative positional error among the six sequences. Thirdly, FAST-LIO2 has the best real-time performance. Our experiments are intended to provide a reference for practitioners in selecting SLAM algorithms. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstract_unstemmed	Abstract Safe autonomous driving is the future trend, and achieving it requires precise and real-time simultaneous localization and mapping (SLAM). Many practitioners are concerned about the performance of LiDAR SLAM algorithms, but there is little research work to evaluate LiDAR SLAM algorithms specifically. This paper evaluates LeGO-LOAM, SC-LeGO-LOAM, LIO-SAM, SC-LIO-SAM, and FAST-LIO2 utilizing the 05-10 sequences from KITTI dataset. The experimental results show that: firstly, there is no significant difference among the absolute trajectory error of the five SLAM algorithms. Secondly, LeGO-LAOM has the smallest relative positional error among the six sequences. Thirdly, FAST-LIO2 has the best real-time performance. Our experiments are intended to provide a reference for practitioners in selecting SLAM algorithms. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
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container_issue	14
title_short	Evaluation of 3D LiDAR SLAM algorithms based on the KITTI dataset
url	https://dx.doi.org/10.1007/s11227-023-05267-3
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author2	Huang, Shihong Yang, Yanxu Zhang, Bingzhi
author2Str	Huang, Shihong Yang, Yanxu Zhang, Bingzhi
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doi_str	10.1007/s11227-023-05267-3
up_date	2024-07-03T14:27:08.836Z
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Evaluation of 3D LiDAR SLAM algorithms based on the KITTI dataset

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