A Review on UAS Trajectory Estimation Using Decentralized Multi-Sensor Systems Based on Robotic Total Stations
In our contribution, we conduct a thematic literature review on trajectory estimation using a decentralized multi-sensor system based on robotic total stations (RTS) with a focus on unmanned aerial system (UAS) platforms. While RTS are commonly used for trajectory estimation in areas where GNSS is n...
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| Abstract | In our contribution, we conduct a thematic literature review on trajectory estimation using a decentralized multi-sensor system based on robotic total stations (RTS) with a focus on unmanned aerial system (UAS) platforms. While RTS are commonly used for trajectory estimation in areas where GNSS is not sufficiently accurate or is unavailable, they are rarely used for UAS trajectory estimation. Extending the RTS with integrated camera images allows for UAS pose estimation (position and orientation). We review existing research on the entire RTS measurement processes, including time synchronization, atmospheric refraction, prism interaction, and RTS-based image evaluation. Additionally, we focus on integrated trajectory estimation using UAS onboard measurements such as IMU and laser scanning data. Although many existing articles address individual steps of the decentralized multi-sensor system, we demonstrate that a combination of existing works related to UAS trajectory estimation and RTS calibration is needed to allow for trajectory estimation at sub-cm and sub-0.01 gon accuracies, and we identify the challenges that must be addressed. Investigations into the use of RTS for kinematic tasks must be extended to realistic distances (approx. 300–500 m) and speeds (>2.5 m s−1). In particular, image acquisition with the integrated camera must be extended by a time synchronization approach. As to the estimation of UAS orientation based on RTS camera images, the results of initial simulation studies must be validated by field tests, and existing approaches for integrated trajectory estimation must be adapted to optimally integrate RTS data. |
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| AbstractList | In our contribution, we conduct a thematic literature review on trajectory estimation using a decentralized multi-sensor system based on robotic total stations (RTS) with a focus on unmanned aerial system (UAS) platforms. While RTS are commonly used for trajectory estimation in areas where GNSS is not sufficiently accurate or is unavailable, they are rarely used for UAS trajectory estimation. Extending the RTS with integrated camera images allows for UAS pose estimation (position and orientation). We review existing research on the entire RTS measurement processes, including time synchronization, atmospheric refraction, prism interaction, and RTS-based image evaluation. Additionally, we focus on integrated trajectory estimation using UAS onboard measurements such as IMU and laser scanning data. Although many existing articles address individual steps of the decentralized multi-sensor system, we demonstrate that a combination of existing works related to UAS trajectory estimation and RTS calibration is needed to allow for trajectory estimation at sub-cm and sub-0.01 gon accuracies, and we identify the challenges that must be addressed. Investigations into the use of RTS for kinematic tasks must be extended to realistic distances (approx. 300–500 m) and speeds (>2.5 m s−1). In particular, image acquisition with the integrated camera must be extended by a time synchronization approach. As to the estimation of UAS orientation based on RTS camera images, the results of initial simulation studies must be validated by field tests, and existing approaches for integrated trajectory estimation must be adapted to optimally integrate RTS data. In our contribution, we conduct a thematic literature review on trajectory estimation using a decentralized multi-sensor system based on robotic total stations (RTS) with a focus on unmanned aerial system (UAS) platforms. While RTS are commonly used for trajectory estimation in areas where GNSS is not sufficiently accurate or is unavailable, they are rarely used for UAS trajectory estimation. Extending the RTS with integrated camera images allows for UAS pose estimation (position and orientation). We review existing research on the entire RTS measurement processes, including time synchronization, atmospheric refraction, prism interaction, and RTS-based image evaluation. Additionally, we focus on integrated trajectory estimation using UAS onboard measurements such as IMU and laser scanning data. Although many existing articles address individual steps of the decentralized multi-sensor system, we demonstrate that a combination of existing works related to UAS trajectory estimation and RTS calibration is needed to allow for trajectory estimation at sub- c m and sub- 0.01 gon accuracies, and we identify the challenges that must be addressed. Investigations into the use of RTS for kinematic tasks must be extended to realistic distances (approx. 300–500 m) and speeds (>2.5 m s−1). In particular, image acquisition with the integrated camera must be extended by a time synchronization approach. As to the estimation of UAS orientation based on RTS camera images, the results of initial simulation studies must be validated by field tests, and existing approaches for integrated trajectory estimation must be adapted to optimally integrate RTS data. In our contribution, we conduct a thematic literature review on trajectory estimation using a decentralized multi-sensor system based on robotic total stations (RTS) with a focus on unmanned aerial system (UAS) platforms. While RTS are commonly used for trajectory estimation in areas where GNSS is not sufficiently accurate or is unavailable, they are rarely used for UAS trajectory estimation. Extending the RTS with integrated camera images allows for UAS pose estimation (position and orientation). We review existing research on the entire RTS measurement processes, including time synchronization, atmospheric refraction, prism interaction, and RTS-based image evaluation. Additionally, we focus on integrated trajectory estimation using UAS onboard measurements such as IMU and laser scanning data. Although many existing articles address individual steps of the decentralized multi-sensor system, we demonstrate that a combination of existing works related to UAS trajectory estimation and RTS calibration is needed to allow for trajectory estimation at sub-cm and sub-0.01 gon accuracies, and we identify the challenges that must be addressed. Investigations into the use of RTS for kinematic tasks must be extended to realistic distances (approx. 300-500 m) and speeds (>2.5 m s-1). In particular, image acquisition with the integrated camera must be extended by a time synchronization approach. As to the estimation of UAS orientation based on RTS camera images, the results of initial simulation studies must be validated by field tests, and existing approaches for integrated trajectory estimation must be adapted to optimally integrate RTS data.In our contribution, we conduct a thematic literature review on trajectory estimation using a decentralized multi-sensor system based on robotic total stations (RTS) with a focus on unmanned aerial system (UAS) platforms. While RTS are commonly used for trajectory estimation in areas where GNSS is not sufficiently accurate or is unavailable, they are rarely used for UAS trajectory estimation. Extending the RTS with integrated camera images allows for UAS pose estimation (position and orientation). We review existing research on the entire RTS measurement processes, including time synchronization, atmospheric refraction, prism interaction, and RTS-based image evaluation. Additionally, we focus on integrated trajectory estimation using UAS onboard measurements such as IMU and laser scanning data. Although many existing articles address individual steps of the decentralized multi-sensor system, we demonstrate that a combination of existing works related to UAS trajectory estimation and RTS calibration is needed to allow for trajectory estimation at sub-cm and sub-0.01 gon accuracies, and we identify the challenges that must be addressed. Investigations into the use of RTS for kinematic tasks must be extended to realistic distances (approx. 300-500 m) and speeds (>2.5 m s-1). In particular, image acquisition with the integrated camera must be extended by a time synchronization approach. As to the estimation of UAS orientation based on RTS camera images, the results of initial simulation studies must be validated by field tests, and existing approaches for integrated trajectory estimation must be adapted to optimally integrate RTS data. In our contribution, we conduct a thematic literature review on trajectory estimation using a decentralized multi-sensor system based on robotic total stations (RTS) with a focus on unmanned aerial system (UAS) platforms. While RTS are commonly used for trajectory estimation in areas where GNSS is not sufficiently accurate or is unavailable, they are rarely used for UAS trajectory estimation. Extending the RTS with integrated camera images allows for UAS pose estimation (position and orientation). We review existing research on the entire RTS measurement processes, including time synchronization, atmospheric refraction, prism interaction, and RTS-based image evaluation. Additionally, we focus on integrated trajectory estimation using UAS onboard measurements such as IMU and laser scanning data. Although many existing articles address individual steps of the decentralized multi-sensor system, we demonstrate that a combination of existing works related to UAS trajectory estimation and RTS calibration is needed to allow for trajectory estimation at sub-cm and sub-0.01 gon accuracies, and we identify the challenges that must be addressed. Investigations into the use of RTS for kinematic tasks must be extended to realistic distances (approx. 300–500 m) and speeds (>2.5 m s[sup.−1] ). In particular, image acquisition with the integrated camera must be extended by a time synchronization approach. As to the estimation of UAS orientation based on RTS camera images, the results of initial simulation studies must be validated by field tests, and existing approaches for integrated trajectory estimation must be adapted to optimally integrate RTS data. In our contribution, we conduct a thematic literature review on trajectory estimation using a decentralized multi-sensor system based on robotic total stations (RTS) with a focus on unmanned aerial system (UAS) platforms. While RTS are commonly used for trajectory estimation in areas where GNSS is not sufficiently accurate or is unavailable, they are rarely used for UAS trajectory estimation. Extending the RTS with integrated camera images allows for UAS pose estimation (position and orientation). We review existing research on the entire RTS measurement processes, including time synchronization, atmospheric refraction, prism interaction, and RTS-based image evaluation. Additionally, we focus on integrated trajectory estimation using UAS onboard measurements such as IMU and laser scanning data. Although many existing articles address individual steps of the decentralized multi-sensor system, we demonstrate that a combination of existing works related to UAS trajectory estimation and RTS calibration is needed to allow for trajectory estimation at sub-cm and sub-0.01 gon accuracies, and we identify the challenges that must be addressed. Investigations into the use of RTS for kinematic tasks must be extended to realistic distances (approx. 300-500 m) and speeds (>2.5 m s ). In particular, image acquisition with the integrated camera must be extended by a time synchronization approach. As to the estimation of UAS orientation based on RTS camera images, the results of initial simulation studies must be validated by field tests, and existing approaches for integrated trajectory estimation must be adapted to optimally integrate RTS data. |
| Audience | Academic |
| Author | Mandlburger, Gottfried Thalmann, Tomas Monetti, David Neuner, Hans-Berndt Dammert, Lucas |
| AuthorAffiliation | 1 Research Units Engineering Geodesy and Photogrammetry, Department Geodesy and Geoinformation, TU Wien, 1040 Vienna, Austria; tomas.thalmann@geo.tuwien.ac.at (T.T.); hans.neuner@geo.tuwien.ac.at (H.-B.N.); gottfried.mandlburger@geo.tuwien.ac.at (G.M.) 2 Skyability GmbH, 7011 Siegendorf, Austria; david.monetti@skyability.com |
| AuthorAffiliation_xml | – name: 1 Research Units Engineering Geodesy and Photogrammetry, Department Geodesy and Geoinformation, TU Wien, 1040 Vienna, Austria; tomas.thalmann@geo.tuwien.ac.at (T.T.); hans.neuner@geo.tuwien.ac.at (H.-B.N.); gottfried.mandlburger@geo.tuwien.ac.at (G.M.) – name: 2 Skyability GmbH, 7011 Siegendorf, Austria; david.monetti@skyability.com |
| Author_xml | – sequence: 1 givenname: Lucas orcidid: 0009-0007-0029-2372 surname: Dammert fullname: Dammert, Lucas – sequence: 2 givenname: Tomas orcidid: 0009-0002-4587-2200 surname: Thalmann fullname: Thalmann, Tomas – sequence: 3 givenname: David surname: Monetti fullname: Monetti, David – sequence: 4 givenname: Hans-Berndt orcidid: 0000-0002-2846-1402 surname: Neuner fullname: Neuner, Hans-Berndt – sequence: 5 givenname: Gottfried orcidid: 0000-0002-2332-293X surname: Mandlburger fullname: Mandlburger, Gottfried |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/40648097$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1515/jag-2022-0027 10.1515/jag-2019-0070 10.20944/preprints202112.0206.v1 10.1016/j.biosystemseng.2016.03.006 10.3390/rs13183564 10.1061/(ASCE)SU.1943-5428.0000208 10.1109/CRV52889.2021.00012 10.5194/isprsarchives-XXXIX-B1-405-2012 10.1117/12.2084221 10.3390/rs15215141 10.1016/j.isprsjprs.2024.06.014 10.33012/2016.14604 10.1080/14498596.2015.1068232 10.1127/1432-8364/2012/0108 10.1007/s12518-024-00593-4 10.3390/drones6110317 10.1016/j.compag.2016.04.019 10.1016/j.isprsjprs.2017.05.008 10.1007/978-3-642-45583-4 10.1109/MFI62651.2024.10705770 10.1515/jag-2024-0040 10.1109/IROS55552.2023.10341529 10.1016/j.isprsjprs.2022.12.022 10.3390/agriculture12060885 10.1371/journal.pone.0251281 10.3390/s17020255 10.1007/BF02026796 10.1515/jag-2016-0028 10.3390/s19163568 10.1016/j.isprsjprs.2025.03.007 10.1016/j.isprsjprs.2006.07.003 10.14358/PERS.82.12.945 10.1109/IPIN.2010.5646270 10.5194/isprsarchives-XXXIX-B7-487-2012 10.33012/2020.17557 10.3390/geomatics2010001 10.1016/j.isprsjprs.2022.04.027 10.1029/2010JD014067 |
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| SubjectTerms | 6-DoF trajectory estimation Accuracy Automation Cameras Comparative analysis Drone aircraft Electronic data processing image-assisted total station Inertial measurement units Investigations Kinematics Lasers Remote sensing Review Robotics sensor synchronization Sensors Technology application UAV |
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| Title | A Review on UAS Trajectory Estimation Using Decentralized Multi-Sensor Systems Based on Robotic Total Stations |
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