Performance Analysis of Visual–Inertial–Range Cooperative Localization for Unmanned Autonomous Vehicle Swarm
The swarm of small UAVs is an emerging technology that will enable abundant cooperative tasks. To tackle the positioning problem for the UAV swarm, cooperative localization (CL) has been intensively studied since it uses relative measurement to improve the positioning availability and accuracy for t...
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MDPI AG
2023-10-01
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| Series: | Drones |
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| Online Access: | https://www.mdpi.com/2504-446X/7/11/651 |
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| author | Jun Lai Suyang Liu Xiaojia Xiang Chaoran Li Dengqing Tang Han Zhou |
| author_facet | Jun Lai Suyang Liu Xiaojia Xiang Chaoran Li Dengqing Tang Han Zhou |
| author_sort | Jun Lai |
| collection | DOAJ |
| description | The swarm of small UAVs is an emerging technology that will enable abundant cooperative tasks. To tackle the positioning problem for the UAV swarm, cooperative localization (CL) has been intensively studied since it uses relative measurement to improve the positioning availability and accuracy for the swarm in GPS-denied environments. Besides relying on inter-UAV range measurement, traditional CL algorithms need to place anchors as location references, which limits their applicability. To implement an infrastructure-less swarm navigation system, a consumer-grade camera together with an inertial device can provide rich environment information, which can be recognized as a kind of local location reference. This paper aims to analyze the fundamental performance of visual–inertial–range CL, which is also a popular metric for UAV planning and sensing optimizing, especially for resource-limited environments. Specifically, a closed-form Fisher information matrix (FIM) of visual–inertial–range CL is constructed in <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi mathvariant="double-struck">R</mi><mi>n</mi></msup><mo>×</mo><mi>SO</mi><mrow><mo>(</mo><mi>n</mi><mo>)</mo></mrow></mrow></semantics></math></inline-formula> manifold. By introducing an <i>equivalent FIM</i> and utilizing of the sparsity of the FIM, the performance of pose estimation can be efficiently calculated. A series of numerical simulations validate its effectiveness for analyzing the CL performance. |
| first_indexed | 2024-03-09T16:54:05Z |
| format | Article |
| id | doaj.art-b79428b97df044fe8fd9d3d09a4a5049 |
| institution | Directory Open Access Journal |
| issn | 2504-446X |
| language | English |
| last_indexed | 2024-03-09T16:54:05Z |
| publishDate | 2023-10-01 |
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| series | Drones |
| spelling | doaj.art-b79428b97df044fe8fd9d3d09a4a50492023-11-24T14:38:05ZengMDPI AGDrones2504-446X2023-10-0171165110.3390/drones7110651Performance Analysis of Visual–Inertial–Range Cooperative Localization for Unmanned Autonomous Vehicle SwarmJun Lai0Suyang Liu1Xiaojia Xiang2Chaoran Li3Dengqing Tang4Han Zhou5College of Artificial Intelligence, National University of Defense Technology, Changsha 410072, ChinaCollege of Artificial Intelligence, National University of Defense Technology, Changsha 410072, ChinaCollege of Artificial Intelligence, National University of Defense Technology, Changsha 410072, ChinaCollege of Artificial Intelligence, National University of Defense Technology, Changsha 410072, ChinaCollege of Artificial Intelligence, National University of Defense Technology, Changsha 410072, ChinaCollege of Artificial Intelligence, National University of Defense Technology, Changsha 410072, ChinaThe swarm of small UAVs is an emerging technology that will enable abundant cooperative tasks. To tackle the positioning problem for the UAV swarm, cooperative localization (CL) has been intensively studied since it uses relative measurement to improve the positioning availability and accuracy for the swarm in GPS-denied environments. Besides relying on inter-UAV range measurement, traditional CL algorithms need to place anchors as location references, which limits their applicability. To implement an infrastructure-less swarm navigation system, a consumer-grade camera together with an inertial device can provide rich environment information, which can be recognized as a kind of local location reference. This paper aims to analyze the fundamental performance of visual–inertial–range CL, which is also a popular metric for UAV planning and sensing optimizing, especially for resource-limited environments. Specifically, a closed-form Fisher information matrix (FIM) of visual–inertial–range CL is constructed in <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi mathvariant="double-struck">R</mi><mi>n</mi></msup><mo>×</mo><mi>SO</mi><mrow><mo>(</mo><mi>n</mi><mo>)</mo></mrow></mrow></semantics></math></inline-formula> manifold. By introducing an <i>equivalent FIM</i> and utilizing of the sparsity of the FIM, the performance of pose estimation can be efficiently calculated. A series of numerical simulations validate its effectiveness for analyzing the CL performance.https://www.mdpi.com/2504-446X/7/11/651small UAV swarmcooperative localizationsimultaneous localization and mappinginfrastructure-less localizationactive sensing |
| spellingShingle | Jun Lai Suyang Liu Xiaojia Xiang Chaoran Li Dengqing Tang Han Zhou Performance Analysis of Visual–Inertial–Range Cooperative Localization for Unmanned Autonomous Vehicle Swarm Drones small UAV swarm cooperative localization simultaneous localization and mapping infrastructure-less localization active sensing |
| title | Performance Analysis of Visual–Inertial–Range Cooperative Localization for Unmanned Autonomous Vehicle Swarm |
| title_full | Performance Analysis of Visual–Inertial–Range Cooperative Localization for Unmanned Autonomous Vehicle Swarm |
| title_fullStr | Performance Analysis of Visual–Inertial–Range Cooperative Localization for Unmanned Autonomous Vehicle Swarm |
| title_full_unstemmed | Performance Analysis of Visual–Inertial–Range Cooperative Localization for Unmanned Autonomous Vehicle Swarm |
| title_short | Performance Analysis of Visual–Inertial–Range Cooperative Localization for Unmanned Autonomous Vehicle Swarm |
| title_sort | performance analysis of visual inertial range cooperative localization for unmanned autonomous vehicle swarm |
| topic | small UAV swarm cooperative localization simultaneous localization and mapping infrastructure-less localization active sensing |
| url | https://www.mdpi.com/2504-446X/7/11/651 |
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