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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2504.07843 |
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| _version_ | 1866913787579203584 |
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| author | Olawoye, Uthman Akhihiero, David Gross, Jason N. |
| author_facet | Olawoye, Uthman Akhihiero, David Gross, Jason N. |
| contents | In this work, we evaluate the use of aerial drone hover constraints in a multisensor fusion of ground robot and drone data to improve the localization performance of a drone. In particular, we build upon our prior work on cooperative localization between an aerial drone and ground robot that fuses data from LiDAR, inertial navigation, peer-to-peer ranging, altimeter, and stereo-vision and evaluate the incorporation knowledge from the autopilot regarding when the drone is hovering. This control command data is leveraged to add constraints on the velocity state. Hover constraints can be considered important dynamic model information, such as the exploitation of zero-velocity updates in pedestrian navigation. We analyze the benefits of these constraints using an incremental factor graph optimization. Experimental data collected in a motion capture faculty is used to provide performance insights and assess the benefits of hover constraints. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_07843 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Experimental Analysis of Quadcopter Drone Hover Constraints for Localization Improvements Olawoye, Uthman Akhihiero, David Gross, Jason N. Robotics In this work, we evaluate the use of aerial drone hover constraints in a multisensor fusion of ground robot and drone data to improve the localization performance of a drone. In particular, we build upon our prior work on cooperative localization between an aerial drone and ground robot that fuses data from LiDAR, inertial navigation, peer-to-peer ranging, altimeter, and stereo-vision and evaluate the incorporation knowledge from the autopilot regarding when the drone is hovering. This control command data is leveraged to add constraints on the velocity state. Hover constraints can be considered important dynamic model information, such as the exploitation of zero-velocity updates in pedestrian navigation. We analyze the benefits of these constraints using an incremental factor graph optimization. Experimental data collected in a motion capture faculty is used to provide performance insights and assess the benefits of hover constraints. |
| title | Experimental Analysis of Quadcopter Drone Hover Constraints for Localization Improvements |
| topic | Robotics |
| url | https://arxiv.org/abs/2504.07843 |