Three Dimensional Intruder Closest Point of Approach Estimation Based-on Monocular Image Parameters in Aircraft Sense and Avoid Motto: ’Almost Everything from Almost Nothing
The paper deals with monocular image-based sense and avoid assuming constant aircraft velocities and straight flight paths. From very limited two dimensional image information it finally characterizes the whole three dimensional collision situation by estimating the time to closest point of approach...
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| Published in | Journal of intelligent & robotic systems Vol. 93; no. 1-2; pp. 261 - 276 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Dordrecht
Springer Netherlands
01.02.2019
Springer |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0921-0296 1573-0409 |
| DOI | 10.1007/s10846-018-0816-6 |
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| Abstract | The paper deals with monocular image-based sense and avoid assuming constant aircraft velocities and straight flight paths. From very limited two dimensional image information it finally characterizes the whole three dimensional collision situation by estimating the time to closest point of approach, the horizontal relative distance and its direction and the vertical relative distance also. The distances are relative to the intruder aircraft horizontal and vertical sizes. The overall estimated relative distance is the closest between the two aircraft in three dimension. So finally, every important information can be extracted to be used in a collision decision. The applicability of the developed method is presented in software-in-the-loop simulation test runs. Several intruder size and speed values are considered together with trajectories covering the whole three dimensional space. The horizontal intruder flight directions relative to the own aircraft cover 360
∘
and the intruder can come from below ar above also. Detailed evaluation and discussion of the results is also included. Finally, the missed detection rate results to be superior (below 3% in every test scenario) though the false alarm rate results a bit high between 7–14%. |
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| AbstractList | The paper deals with monocular image-based sense and avoid assuming constant aircraft velocities and straight flight paths. From very limited two dimensional image information it finally characterizes the whole three dimensional collision situation by estimating the time to closest point of approach, the horizontal relative distance and its direction and the vertical relative distance also. The distances are relative to the intruder aircraft horizontal and vertical sizes. The overall estimated relative distance is the closest between the two aircraft in three dimension. So finally, every important information can be extracted to be used in a collision decision. The applicability of the developed method is presented in software-in-the-loop simulation test runs. Several intruder size and speed values are considered together with trajectories covering the whole three dimensional space. The horizontal intruder flight directions relative to the own aircraft cover 360[degrees] and the intruder can come from below ar above also. Detailed evaluation and discussion of the results is also included. Finally, the missed detection rate results to be superior (below 3% in every test scenario) though the false alarm rate results a bit high between 7-14%. Keywords Sense and avoid * Monocular camera * Closest point of approach * Intruder direction Mathematics Subject Classification (2010) 93C41 * 93A30 * 93C85 The paper deals with monocular image-based sense and avoid assuming constant aircraft velocities and straight flight paths. From very limited two dimensional image information it finally characterizes the whole three dimensional collision situation by estimating the time to closest point of approach, the horizontal relative distance and its direction and the vertical relative distance also. The distances are relative to the intruder aircraft horizontal and vertical sizes. The overall estimated relative distance is the closest between the two aircraft in three dimension. So finally, every important information can be extracted to be used in a collision decision. The applicability of the developed method is presented in software-in-the-loop simulation test runs. Several intruder size and speed values are considered together with trajectories covering the whole three dimensional space. The horizontal intruder flight directions relative to the own aircraft cover 360 ∘ and the intruder can come from below ar above also. Detailed evaluation and discussion of the results is also included. Finally, the missed detection rate results to be superior (below 3% in every test scenario) though the false alarm rate results a bit high between 7–14%. |
| Audience | Academic |
| Author | Bokor, Jozsef Bauer, Peter Hiba, Antal Zarandy, Akos |
| Author_xml | – sequence: 1 givenname: Peter orcidid: 0000-0002-1925-2270 surname: Bauer fullname: Bauer, Peter email: bauer.peter@sztaki.mta.hu organization: Systems and Control Laboratory, Institute for Computer Science and Control, Hungarian Academy of Sciences (MTA SZTAKI) – sequence: 2 givenname: Antal surname: Hiba fullname: Hiba, Antal organization: Computational Optical Sensing and Processing Laboratory, Institute for Computer Science and Control, Hungarian Academy of Sciences (MTA SZTAKI) – sequence: 3 givenname: Jozsef surname: Bokor fullname: Bokor, Jozsef organization: Systems and Control Laboratory, Institute for Computer Science and Control, Hungarian Academy of Sciences (MTA SZTAKI) – sequence: 4 givenname: Akos surname: Zarandy fullname: Zarandy, Akos organization: Computational Optical Sensing and Processing Laboratory, Institute for Computer Science and Control, Hungarian Academy of Sciences (MTA SZTAKI) |
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| Keywords | Intruder direction Sense and avoid Monocular camera Closest point of approach 93C41 93C85 93A30 |
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| References | Fasano, G., Accardo, D., Forlenza, L., Moccia, A., Rispoli, A.: A multi-sensor obstacle detection and tracking system for autonomousuav sense and avoid. In: XX Congresso Nazionale AIDAA, Milano (2009) ZsedrovitsTBauerPPenczBJMHibaAGőzseIKisantalMNémethMNagyZVanekBZarándyABokorJOnboard visual sense and avoid system for small aircraftIEEE A&E Syst. Mag.201631182710.1109/MAES.2016.150129 Mori, T., Scherer, S.: First results in detecting and avoiding frontal obstacles from a monocular camera for micro unmanned aerial vehicles. In: International Conference on Robotics and Automation (2013) NussbergerAGrabnerHGoolLVFeature article: Robust Aerial Object Tracking from an Airborne platformIEEE Aerosp. Electron. Syst. Mag.2016317384610.1109/MAES.2016.150126https://doi.org/10.1109/MAES.2016.150126 https://doi.org/10.1109/MAES.2016.150126 airliners.net: aircraft-data. http://www.airliners.net/aircraft-data/ (2015) Degen, S.: Reactive image-based collision avoidance system for unmanned aircraft systems. Master’s thesis, Australian Research Centre for Aerospace Automation (2011) Meyer, F., Bouthemy, P.: Estimation of time-to-collision maps from first order motion models and normal flows. In: Proc. of 11th IAPR International Conference on Pattern Recognition (1992) Shakernia, O., Chen, W.-Z., Raska, V.: Passive ranging for uav sense and avoid applications. In: fotech Aerospace (2005) MelnykRSchrageDVolovoiVJimenezHSense and avoid requirements for unmanned aircraft systems using a target level of safety approachRisk Anal.201434101894190610.1111/risa.12200 EU: Roadmap for the integration of civil Remotely-Piloted Aircraft Systems into the European Aviation System. Tech. rep., European RPAS Steering Group (2013) MejiasLMcFadyenAFordJJFeature article: Sense and avoid technology developments at Queensland university of technologyIEEE Aerosp. Electron. Syst. Mag.2016317283710.1109/MAES.2016.150157https://doi.org/10.1109/MAES.2016.150157 FAA: Federal Aviation Regulation 14 CFR Part 91. Federal Aviation Administration (FAA) (2016) Bauer, P., Hiba, A., Vanek, B., Zarandy, A., Bokor, J.: Monocular image-based time to collision and closest point of approach estimation. In: proceedings of 24th Mediterranean Conference on Control and Automation (MED’16). Athens, Greece (2016) Zarandy, A., Nagy, Z., Vanek, B., Zsedrovits, T., Kiss, A., Nemeth, M.: A five-camera vision system for uav visual attitude calculation and collision warning. In: Computer Vision Systems, Lecture Notes in Computer Science, pp. 11–20. Saint Petersburg, Russia (2013) Salazar, L.R., Sabatini, R., Ramasamy, S., Gardi, A.: A novel system for non-cooperative uav sense-and-avoid. In: Proceedings of european navigation conference 2013 (ENC 2013) (2013) Byrne, J., Taylor, C.J.: Expansion segmentation for visual collision detection and estimation. In: Proc. of IEEE International Conference on Robotics and Automation (2009) Schaub, A., Burschka, D.: Spatio-temporal prediction of collision candidates for static and dynamic objects in monocular image sequences. In Proc. of IEEE Intelligent Vehicles Symposium (IV 2013) (2013) ZsedrovitsTZarandyAVanekBPeniTBokorJRoskaTCollision avoidance for UAV using visual detection. In: Proceedings of IEEE ISCAS 2011 (International Symposium on Circuits and Systems), pp. 2173–2176. IEEE, Rio de Janeiro201110.1109/ISCAS.2011.5938030 LyuYPanQZhaoCZhangYHuJFeature article: Vision-based UAV collision avoidance with 2D dynamic safety envelopeIEEE Aerosp. Electron. Syst. Mag.2016317162610.1109/MAES.2016.150155https://doi.org/10.1109/MAES.2016.150155 Bauer, P., Hiba, A.: Vision only collision detection with omnidirectional multi-camera system. In: Proc. of the 20th World Congress of the International Federation of Automatic Control, pp. 15,780–15,785. IFAC, Toulouse, France (2017) Dempsey, M.: U.s. army unmanned aircraft systems roadmap 2010-2035. Tech. rep., U.S. Army UAS Center of Excellence (2010) Bauer, P., Vanek, B., Pni, T., Zsedrovits, T., Pencz, B., Zarndy, K., Bokor, J.: Aircraft trajectory tracking with large sideslip angles for sense and avoid intruder state estimation. In proceedings of 22nd Mediterranean Conference on Control and Automation (MED’14). Palermo, Italy (2014) ChoiHKimYHwangIReactive collision avoidance of unmanned aerial vehicles using a single vision sensorJ. Guid. Control. Dyn.20133641234124010.2514/1.57131https://doi.org/10.2514/1.57131 https://doi.org/10.2514/1.57131 Hutchings, T., Jeffryes, S., Farmer, S.J.: Architecting uav sense & avoid systems. In: Proc. Institution of Engineering and Technology Conf. Autonomous Systems, pp. 1–8 (2007) Bauer, P., Hiba, A., Bokor, J.: Monocular image-based intruder direction estimation at closest point of approach. In: Proc. of the International Conference on Unmanned Aircraft Systems (ICUAS) 2017, pp. 1108–1117, ICUAS Association, Miami, FL, USA (2017) Speijker, L., Verstraeten, J., Kranenburg, C., van der Geest, P.: Scoping improvements to ’See And Avoid’ for general aviation (SISA). Tech. rep., European Aviation Safety Agency (EASA (2012) Watanabe, Y.: Stochastically optimized monocular vision-based navigation and guidance. Ph.D. thesis, Georgia Institute of Technology (2008) FasanoGAccardoDTirriAEMocciaAFeature article: Experimental analysis of onboard non-cooperative sense and avoid solutions based on radar, optical sensors, and data fusionIEEE Aerosp. Electron. Syst. Mag.201631761410.1109/MAES.2016.150164https://doi.org/10.1109/MAES.2016.150164 Forlenza, L.: Vision based strategies for implementing Sense and Avoid capabilities onboard Unmanned Aerial Systems. Ph.D. thesis, UNIVERSITÁ DEGLI STUDI DI NAPOLI FEDERICO II (2012) Bauer, P., Vanek, B., Peni, T., Futaki, A., Pencz, B., Zarandy, A., Bokor, J.: Monocular image parameter-based aircraft sense and avoid. In: proceedings of 23rd Mediterranean Conference on Control and Automation (MED’15). Torremolinos, Spain (2015) Frew, E.W.: Observer trajectory generation for target-motion estimation using monocular vision. Ph.D. thesis, Stanford University (2003) JamoomMBJoergerMPervanBUnmanned aircraft system sense-and-avoid integrity and continuity riskJ. Guid. Control. Dyn.201539349850910.2514/1.G001468https://doi.org/10.2514/1.G001468 Vanek, B., Peni, T., Zarandy, A., Bokor, J., Zsedrovits, T., Roska, T.: Performance characteristics of a complete vision only sense and avoid system. In: Proceedings of AIAA GNC 2012 (Guidance, Navigation and Control Conference), AIAA 2012-4703, pp. 1–15, Minneapolis, Minnesota (2012) 816_CR23 816_CR22 816_CR28 H Choi (816_CR8) 2013; 36 L Mejias (816_CR20) 2016; 31 816_CR27 816_CR26 816_CR25 G Fasano (816_CR14) 2016; 31 816_CR29 R Melnyk (816_CR21) 2014; 34 816_CR9 T Zsedrovits (816_CR33) 2011 816_CR7 816_CR6 816_CR5 Y Lyu (816_CR19) 2016; 31 816_CR4 816_CR3 816_CR2 816_CR1 816_CR13 816_CR12 816_CR11 816_CR10 816_CR17 816_CR16 816_CR15 MB Jamoom (816_CR18) 2015; 39 A Nussberger (816_CR24) 2016; 31 T Zsedrovits (816_CR32) 2016; 31 816_CR31 816_CR30 |
| References_xml | – reference: Watanabe, Y.: Stochastically optimized monocular vision-based navigation and guidance. Ph.D. thesis, Georgia Institute of Technology (2008) – reference: Bauer, P., Hiba, A.: Vision only collision detection with omnidirectional multi-camera system. In: Proc. of the 20th World Congress of the International Federation of Automatic Control, pp. 15,780–15,785. IFAC, Toulouse, France (2017) – reference: FasanoGAccardoDTirriAEMocciaAFeature article: Experimental analysis of onboard non-cooperative sense and avoid solutions based on radar, optical sensors, and data fusionIEEE Aerosp. Electron. Syst. Mag.201631761410.1109/MAES.2016.150164https://doi.org/10.1109/MAES.2016.150164 – reference: EU: Roadmap for the integration of civil Remotely-Piloted Aircraft Systems into the European Aviation System. Tech. rep., European RPAS Steering Group (2013) – reference: Zarandy, A., Nagy, Z., Vanek, B., Zsedrovits, T., Kiss, A., Nemeth, M.: A five-camera vision system for uav visual attitude calculation and collision warning. In: Computer Vision Systems, Lecture Notes in Computer Science, pp. 11–20. Saint Petersburg, Russia (2013) – reference: Bauer, P., Vanek, B., Peni, T., Futaki, A., Pencz, B., Zarandy, A., Bokor, J.: Monocular image parameter-based aircraft sense and avoid. In: proceedings of 23rd Mediterranean Conference on Control and Automation (MED’15). Torremolinos, Spain (2015) – reference: Frew, E.W.: Observer trajectory generation for target-motion estimation using monocular vision. Ph.D. thesis, Stanford University (2003) – reference: Bauer, P., Hiba, A., Bokor, J.: Monocular image-based intruder direction estimation at closest point of approach. In: Proc. of the International Conference on Unmanned Aircraft Systems (ICUAS) 2017, pp. 1108–1117, ICUAS Association, Miami, FL, USA (2017) – reference: Hutchings, T., Jeffryes, S., Farmer, S.J.: Architecting uav sense & avoid systems. In: Proc. Institution of Engineering and Technology Conf. Autonomous Systems, pp. 1–8 (2007) – reference: Meyer, F., Bouthemy, P.: Estimation of time-to-collision maps from first order motion models and normal flows. In: Proc. of 11th IAPR International Conference on Pattern Recognition (1992) – reference: FAA: Federal Aviation Regulation 14 CFR Part 91. Federal Aviation Administration (FAA) (2016) – reference: JamoomMBJoergerMPervanBUnmanned aircraft system sense-and-avoid integrity and continuity riskJ. Guid. Control. Dyn.201539349850910.2514/1.G001468https://doi.org/10.2514/1.G001468 – reference: Dempsey, M.: U.s. army unmanned aircraft systems roadmap 2010-2035. Tech. rep., U.S. Army UAS Center of Excellence (2010) – reference: Vanek, B., Peni, T., Zarandy, A., Bokor, J., Zsedrovits, T., Roska, T.: Performance characteristics of a complete vision only sense and avoid system. In: Proceedings of AIAA GNC 2012 (Guidance, Navigation and Control Conference), AIAA 2012-4703, pp. 1–15, Minneapolis, Minnesota (2012) – reference: Schaub, A., Burschka, D.: Spatio-temporal prediction of collision candidates for static and dynamic objects in monocular image sequences. In Proc. of IEEE Intelligent Vehicles Symposium (IV 2013) (2013) – reference: LyuYPanQZhaoCZhangYHuJFeature article: Vision-based UAV collision avoidance with 2D dynamic safety envelopeIEEE Aerosp. Electron. Syst. Mag.2016317162610.1109/MAES.2016.150155https://doi.org/10.1109/MAES.2016.150155 – reference: Shakernia, O., Chen, W.-Z., Raska, V.: Passive ranging for uav sense and avoid applications. In: fotech Aerospace (2005) – reference: Bauer, P., Hiba, A., Vanek, B., Zarandy, A., Bokor, J.: Monocular image-based time to collision and closest point of approach estimation. In: proceedings of 24th Mediterranean Conference on Control and Automation (MED’16). Athens, Greece (2016) – reference: MejiasLMcFadyenAFordJJFeature article: Sense and avoid technology developments at Queensland university of technologyIEEE Aerosp. Electron. Syst. Mag.2016317283710.1109/MAES.2016.150157https://doi.org/10.1109/MAES.2016.150157 – reference: Mori, T., Scherer, S.: First results in detecting and avoiding frontal obstacles from a monocular camera for micro unmanned aerial vehicles. In: International Conference on Robotics and Automation (2013) – reference: Salazar, L.R., Sabatini, R., Ramasamy, S., Gardi, A.: A novel system for non-cooperative uav sense-and-avoid. In: Proceedings of european navigation conference 2013 (ENC 2013) (2013) – reference: Degen, S.: Reactive image-based collision avoidance system for unmanned aircraft systems. Master’s thesis, Australian Research Centre for Aerospace Automation (2011) – reference: airliners.net: aircraft-data. http://www.airliners.net/aircraft-data/ (2015) – reference: NussbergerAGrabnerHGoolLVFeature article: Robust Aerial Object Tracking from an Airborne platformIEEE Aerosp. Electron. Syst. Mag.2016317384610.1109/MAES.2016.150126https://doi.org/10.1109/MAES.2016.150126 https://doi.org/10.1109/MAES.2016.150126 – reference: ZsedrovitsTZarandyAVanekBPeniTBokorJRoskaTCollision avoidance for UAV using visual detection. In: Proceedings of IEEE ISCAS 2011 (International Symposium on Circuits and Systems), pp. 2173–2176. IEEE, Rio de Janeiro201110.1109/ISCAS.2011.5938030 – reference: Forlenza, L.: Vision based strategies for implementing Sense and Avoid capabilities onboard Unmanned Aerial Systems. Ph.D. thesis, UNIVERSITÁ DEGLI STUDI DI NAPOLI FEDERICO II (2012) – reference: Fasano, G., Accardo, D., Forlenza, L., Moccia, A., Rispoli, A.: A multi-sensor obstacle detection and tracking system for autonomousuav sense and avoid. In: XX Congresso Nazionale AIDAA, Milano (2009) – reference: Speijker, L., Verstraeten, J., Kranenburg, C., van der Geest, P.: Scoping improvements to ’See And Avoid’ for general aviation (SISA). Tech. rep., European Aviation Safety Agency (EASA (2012) – reference: ZsedrovitsTBauerPPenczBJMHibaAGőzseIKisantalMNémethMNagyZVanekBZarándyABokorJOnboard visual sense and avoid system for small aircraftIEEE A&E Syst. Mag.201631182710.1109/MAES.2016.150129 – reference: Byrne, J., Taylor, C.J.: Expansion segmentation for visual collision detection and estimation. In: Proc. of IEEE International Conference on Robotics and Automation (2009) – reference: MelnykRSchrageDVolovoiVJimenezHSense and avoid requirements for unmanned aircraft systems using a target level of safety approachRisk Anal.201434101894190610.1111/risa.12200 – reference: ChoiHKimYHwangIReactive collision avoidance of unmanned aerial vehicles using a single vision sensorJ. Guid. Control. Dyn.20133641234124010.2514/1.57131https://doi.org/10.2514/1.57131 https://doi.org/10.2514/1.57131 – reference: Bauer, P., Vanek, B., Pni, T., Zsedrovits, T., Pencz, B., Zarndy, K., Bokor, J.: Aircraft trajectory tracking with large sideslip angles for sense and avoid intruder state estimation. In proceedings of 22nd Mediterranean Conference on Control and Automation (MED’14). 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| SubjectTerms | Artificial Intelligence Control Electrical Engineering Engineering Mechanical Engineering Mechatronics Robotics Simulation methods |
| Subtitle | Motto: ’Almost Everything from Almost Nothing |
| Title | Three Dimensional Intruder Closest Point of Approach Estimation Based-on Monocular Image Parameters in Aircraft Sense and Avoid |
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