Probabilistic qualitative mapping for robots
A probabilistic qualitative relational mapping (PQRM) algorithm is developed to enable robots to robustly map environments using noisy sensor measurements. Qualitative state representations provide soft, relative map information which is robust to metrical errors. In this paper, probabilistic distri...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Padgett, Jennifer [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2017 |
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| Schlagwörter: |
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| Umfang: |
15 |
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| Übergeordnetes Werk: |
Enthalten in: Comparison of LI-RADS with other non-invasive liver MRI criteria and radiological opinion for diagnosing hepatocellular carcinoma in cirrhotic livers using gadoxetic acid with histopathological explant correlation - Clarke, C.G.D. ELSEVIER, 2021, international journal, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:98 ; year:2017 ; pages:292-306 ; extent:15 |
| Links: |
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| DOI / URN: |
10.1016/j.robot.2017.09.013 |
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ELV040824187 |
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| 520 | |a A probabilistic qualitative relational mapping (PQRM) algorithm is developed to enable robots to robustly map environments using noisy sensor measurements. Qualitative state representations provide soft, relative map information which is robust to metrical errors. In this paper, probabilistic distributions over qualitative states are derived and an algorithm to update the map recursively is developed. Maps are evaluated for convergence and correctness in Monte Carlo simulations. Validation tests are conducted on the New College dataset to evaluate map performance in realistic environments. | ||
| 650 | 7 | |a Qualitative relational mapping |2 Elsevier | |
| 650 | 7 | |a Spatial reasoning |2 Elsevier | |
| 650 | 7 | |a Robotic mapping |2 Elsevier | |
| 700 | 1 | |a Campbell, Mark |4 oth | |
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A probabilistic qualitative relational mapping (PQRM) algorithm is developed to enable robots to robustly map environments using noisy sensor measurements. Qualitative state representations provide soft, relative map information which is robust to metrical errors. In this paper, probabilistic distributions over qualitative states are derived and an algorithm to update the map recursively is developed. Maps are evaluated for convergence and correctness in Monte Carlo simulations. Validation tests are conducted on the New College dataset to evaluate map performance in realistic environments. |
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A probabilistic qualitative relational mapping (PQRM) algorithm is developed to enable robots to robustly map environments using noisy sensor measurements. Qualitative state representations provide soft, relative map information which is robust to metrical errors. In this paper, probabilistic distributions over qualitative states are derived and an algorithm to update the map recursively is developed. Maps are evaluated for convergence and correctness in Monte Carlo simulations. Validation tests are conducted on the New College dataset to evaluate map performance in realistic environments. |
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A probabilistic qualitative relational mapping (PQRM) algorithm is developed to enable robots to robustly map environments using noisy sensor measurements. Qualitative state representations provide soft, relative map information which is robust to metrical errors. In this paper, probabilistic distributions over qualitative states are derived and an algorithm to update the map recursively is developed. Maps are evaluated for convergence and correctness in Monte Carlo simulations. Validation tests are conducted on the New College dataset to evaluate map performance in realistic environments. |
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