Enhance Accuracy: Sensitivity and Uncertainty Theory in LiDAR Odometry and Mapping Article Swipe
Zeyu Wan
,
Yu Zhang
,
Bin He
,
Zhuofan Cui
,
Weichen Dai
,
Lipu Zhou
,
Guoquan Huang
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2111.07723
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2111.07723
Currently, the improvement of LiDAR poses estimation accuracy is an urgent need for mobile robots. Research indicates that diverse LiDAR points have different influences on the accuracy of pose estimation. This study aimed to select a good point set to enhance accuracy. Accordingly, the sensitivity and uncertainty of LiDAR point residuals were formulated as a fundamental basis for derivation and analysis. High-sensitivity and low -uncertainty point residual terms are preferred to achieve higher pose estimation accuracy. The proposed selection method has been theoretically proven to be capable of achieving a global statistical optimum. It was tested on artificial data and compared with the KITTI benchmark. It was also implemented in LiDAR odometry (LO) and LiDAR inertial odometry (LIO), both indoors and outdoors. The experiments revealed that utilizing selected LiDAR point residuals simultaneously enhances optimization accuracy, decreases residual terms, and guarantees real-time performance.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2111.07723
- https://arxiv.org/pdf/2111.07723
- OA Status
- green
- Cited By
- 4
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4292200098
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4292200098Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2111.07723Digital Object Identifier
- Title
-
Enhance Accuracy: Sensitivity and Uncertainty Theory in LiDAR Odometry and MappingWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-11-15Full publication date if available
- Authors
-
Zeyu Wan, Yu Zhang, Bin He, Zhuofan Cui, Weichen Dai, Lipu Zhou, Guoquan HuangList of authors in order
- Landing page
-
https://arxiv.org/abs/2111.07723Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2111.07723Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2111.07723Direct OA link when available
- Concepts
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Lidar, Odometry, Sensitivity (control systems), Benchmark (surveying), Computer science, Residual, Ranging, Artificial intelligence, Point (geometry), Remote sensing, Computer vision, Mobile robot, Robot, Algorithm, Mathematics, Geography, Engineering, Geodesy, Geometry, Electronic engineering, TelecommunicationsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
4Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2024: 1, 2023: 1, 2022: 1Per-year citation counts (last 5 years)
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.have | 21 |
| abstract_inverted_index.need | 11 |
| abstract_inverted_index.pose | 28, 73 |
| abstract_inverted_index.that | 17, 125 |
| abstract_inverted_index.were | 51 |
| abstract_inverted_index.with | 101 |
| abstract_inverted_index.KITTI | 103 |
| abstract_inverted_index.LiDAR | 4, 19, 48, 110, 114, 128 |
| abstract_inverted_index.aimed | 32 |
| abstract_inverted_index.basis | 56 |
| abstract_inverted_index.point | 37, 49, 65, 129 |
| abstract_inverted_index.poses | 5 |
| abstract_inverted_index.study | 31 |
| abstract_inverted_index.terms | 67 |
| abstract_inverted_index.(LIO), | 117 |
| abstract_inverted_index.global | 90 |
| abstract_inverted_index.higher | 72 |
| abstract_inverted_index.method | 79 |
| abstract_inverted_index.mobile | 13 |
| abstract_inverted_index.points | 20 |
| abstract_inverted_index.proven | 83 |
| abstract_inverted_index.select | 34 |
| abstract_inverted_index.terms, | 137 |
| abstract_inverted_index.tested | 95 |
| abstract_inverted_index.urgent | 10 |
| abstract_inverted_index.achieve | 71 |
| abstract_inverted_index.capable | 86 |
| abstract_inverted_index.diverse | 18 |
| abstract_inverted_index.enhance | 40 |
| abstract_inverted_index.indoors | 119 |
| abstract_inverted_index.robots. | 14 |
| abstract_inverted_index.Research | 15 |
| abstract_inverted_index.accuracy | 7, 26 |
| abstract_inverted_index.compared | 100 |
| abstract_inverted_index.enhances | 132 |
| abstract_inverted_index.inertial | 115 |
| abstract_inverted_index.odometry | 111, 116 |
| abstract_inverted_index.optimum. | 92 |
| abstract_inverted_index.proposed | 77 |
| abstract_inverted_index.residual | 66, 136 |
| abstract_inverted_index.revealed | 124 |
| abstract_inverted_index.selected | 127 |
| abstract_inverted_index.accuracy, | 134 |
| abstract_inverted_index.accuracy. | 41, 75 |
| abstract_inverted_index.achieving | 88 |
| abstract_inverted_index.analysis. | 60 |
| abstract_inverted_index.decreases | 135 |
| abstract_inverted_index.different | 22 |
| abstract_inverted_index.indicates | 16 |
| abstract_inverted_index.outdoors. | 121 |
| abstract_inverted_index.preferred | 69 |
| abstract_inverted_index.real-time | 140 |
| abstract_inverted_index.residuals | 50, 130 |
| abstract_inverted_index.selection | 78 |
| abstract_inverted_index.utilizing | 126 |
| abstract_inverted_index.Currently, | 0 |
| abstract_inverted_index.artificial | 97 |
| abstract_inverted_index.benchmark. | 104 |
| abstract_inverted_index.derivation | 58 |
| abstract_inverted_index.estimation | 6, 74 |
| abstract_inverted_index.formulated | 52 |
| abstract_inverted_index.guarantees | 139 |
| abstract_inverted_index.influences | 23 |
| abstract_inverted_index.estimation. | 29 |
| abstract_inverted_index.experiments | 123 |
| abstract_inverted_index.fundamental | 55 |
| abstract_inverted_index.implemented | 108 |
| abstract_inverted_index.improvement | 2 |
| abstract_inverted_index.sensitivity | 44 |
| abstract_inverted_index.statistical | 91 |
| abstract_inverted_index.uncertainty | 46 |
| abstract_inverted_index.-uncertainty | 64 |
| abstract_inverted_index.Accordingly, | 42 |
| abstract_inverted_index.optimization | 133 |
| abstract_inverted_index.performance. | 141 |
| abstract_inverted_index.theoretically | 82 |
| abstract_inverted_index.simultaneously | 131 |
| abstract_inverted_index.High-sensitivity | 61 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile |