The hybrid Cramér-Rao lower bound for simultaneous self-localization and room geometry estimation
Abstract This paper addresses the problem of tracking a moving source, e.g., a robot, equipped with both receivers and a source, that is tracking its own location and simultaneously estimating the locations of multiple plane reflectors. We assume a noisy knowledge of the robot’s movement. We formula...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2021-01-01
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| Series: | EURASIP Journal on Advances in Signal Processing |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13634-020-00702-6 |
| _version_ | 1818937125592104960 |
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| author | Maya Veisman Yair Noam Sharon Gannot |
| author_facet | Maya Veisman Yair Noam Sharon Gannot |
| author_sort | Maya Veisman |
| collection | DOAJ |
| description | Abstract This paper addresses the problem of tracking a moving source, e.g., a robot, equipped with both receivers and a source, that is tracking its own location and simultaneously estimating the locations of multiple plane reflectors. We assume a noisy knowledge of the robot’s movement. We formulate this problem, which is also known as simultaneous localization and mapping (SLAM), as a hybrid estimation problem. We derive the extended Kalman filter (EKF) for both tracking the robot’s own location and estimating the room geometry. Since the EKF employs linearization at every step, we incorporate a regulated kinematic model, which facilitates a successful tracking. In addition, we consider the echo-labeling problem as solved and beyond the scope of this paper. We then develop the hybrid Cramér-Rao lower bound on the estimation accuracy of both the localization and mapping parameters. The algorithm is evaluated with respect to the bound via simulations, which shows that the EKF approaches the hybrid Cramér-Rao bound (CRB) (HCRB) as the number of observation increases. This result implies that for the examples tested in simulation, the HCRB is an asymptotically tight bound and that the EKF is an optimal estimator. Whether this property is true in general remains an open question. |
| first_indexed | 2024-12-20T05:46:59Z |
| format | Article |
| id | doaj.art-d9d188dab150478081bb0ae2d66a2a8e |
| institution | Directory Open Access Journal |
| issn | 1687-6180 |
| language | English |
| last_indexed | 2024-12-20T05:46:59Z |
| publishDate | 2021-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EURASIP Journal on Advances in Signal Processing |
| spelling | doaj.art-d9d188dab150478081bb0ae2d66a2a8e2022-12-21T19:51:17ZengSpringerOpenEURASIP Journal on Advances in Signal Processing1687-61802021-01-012021112210.1186/s13634-020-00702-6The hybrid Cramér-Rao lower bound for simultaneous self-localization and room geometry estimationMaya Veisman0Yair Noam1Sharon Gannot2Bar-Ilan UniversityBar-Ilan UniversityBar-Ilan UniversityAbstract This paper addresses the problem of tracking a moving source, e.g., a robot, equipped with both receivers and a source, that is tracking its own location and simultaneously estimating the locations of multiple plane reflectors. We assume a noisy knowledge of the robot’s movement. We formulate this problem, which is also known as simultaneous localization and mapping (SLAM), as a hybrid estimation problem. We derive the extended Kalman filter (EKF) for both tracking the robot’s own location and estimating the room geometry. Since the EKF employs linearization at every step, we incorporate a regulated kinematic model, which facilitates a successful tracking. In addition, we consider the echo-labeling problem as solved and beyond the scope of this paper. We then develop the hybrid Cramér-Rao lower bound on the estimation accuracy of both the localization and mapping parameters. The algorithm is evaluated with respect to the bound via simulations, which shows that the EKF approaches the hybrid Cramér-Rao bound (CRB) (HCRB) as the number of observation increases. This result implies that for the examples tested in simulation, the HCRB is an asymptotically tight bound and that the EKF is an optimal estimator. Whether this property is true in general remains an open question.https://doi.org/10.1186/s13634-020-00702-6SLAMSpeaker localization and trackingRoom mappingHybrid Cramér-Rao lower bound |
| spellingShingle | Maya Veisman Yair Noam Sharon Gannot The hybrid Cramér-Rao lower bound for simultaneous self-localization and room geometry estimation EURASIP Journal on Advances in Signal Processing SLAM Speaker localization and tracking Room mapping Hybrid Cramér-Rao lower bound |
| title | The hybrid Cramér-Rao lower bound for simultaneous self-localization and room geometry estimation |
| title_full | The hybrid Cramér-Rao lower bound for simultaneous self-localization and room geometry estimation |
| title_fullStr | The hybrid Cramér-Rao lower bound for simultaneous self-localization and room geometry estimation |
| title_full_unstemmed | The hybrid Cramér-Rao lower bound for simultaneous self-localization and room geometry estimation |
| title_short | The hybrid Cramér-Rao lower bound for simultaneous self-localization and room geometry estimation |
| title_sort | hybrid cramer rao lower bound for simultaneous self localization and room geometry estimation |
| topic | SLAM Speaker localization and tracking Room mapping Hybrid Cramér-Rao lower bound |
| url | https://doi.org/10.1186/s13634-020-00702-6 |
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