Depth map improvements for stereo-based depth cameras on drones

• Autorzy: Pohl Daniel , Dorodnicov Sergey , Achtelik Markus

Identyfikatory

DOI

10.15439/2019F66

• Konferencja: Federated Conference on Computer Science and Information Systems (14 ; 01-04.09.2019 ; Leipzig, Germany)
• Języki publikacji: EN

Abstrakty

EN

Using stereo-based depth cameras outdoors on drones can lead to challenging situations for stereo algorithms calculating a depth map. A false depth value indicating an object close to the drone can confuse obstacle avoidance algorithms and lead to erratic behavior during the drone flight. We analyze the encountered issues from real-world tests together with practical solutions including a post-processing method to modify depth maps against outliers with wrong depth values.

Słowa kluczowe

EN

depth camera; stereo; computer vision

PL

kamera; stereo; wizja komputerowa

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2019
• Tom: Vol. 18
• Strony: 341--–348
• Opis fizyczny: Bibliogr. 18 poz., il.

Twórcy

autor

Pohl Daniel

• Intel Corporation, Konrad-Zuse-Bogen 4, Krailling, Germany

autor

Dorodnicov Sergey

• Intel Corporation, Rachel 4, Haifa, Israel

autor

Achtelik Markus

• Intel Corporation, Konrad-Zuse-Bogen 4, Krailling, Germany

Bibliografia

• 1. N. Gageik, T. Müller, and S. Montenegro, “Obstacle Detection and Collision Avoidance using Ultrasonic Distance Sensors for an Autonomous Quadrocopter”, University of Wurzburg, Aerospace information Technology Wurzburg, pp. 3–23, 2012.
• 2. L. Wallace, A. Lucieer, C. Watson, and D. Turner, “Development of a UAV-LiDAR System with Application to Forest Inventory”, Remote Sensing, vol. 4, no. 6, pp. 1519–1543, 2012. DOI : 10.3390/rs4061519.
• 3. A. Ferrick, J. Fish, E. Venator, and G. S. Lee, “UAV Obstacle Avoidance using Image Processing Techniques”, in IEEE International Conference on Technologies for Practical Robot Applications (TePRA), 2012, pp. 73–78. DOI : 10.1109/TePRA.2012.6215657.
• 4. K. B. Ariyur, P. Lommel, and D. F. Enns, “Reactive Inflight Obstacle Avoidance via Radar Feedback”, in Proceedings of the 2005 American Control Conference, IEEE, pp. 2978–2982. http://dx.doi.org/10.1109/ACC.2005.1470427.
• 5. K Boudjit, C Larbes, and M Alouache, “Control of Flight Operation of a Quad rotor AR. Drone Using Depth Map from Microsoft Kinect Sensor”, International Journal of Engineering and Innovative Technology (IJEIT), vol. 3, pp. 15–19, 2013.
• 6. A Deris, I Trigonis, A Aravanis, and E. Stathopoulou, “Depth cameras on UAVs: A first approach”, The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. 42, p. 231, 2017. DOI : 10.5194/isprs-archives-XLII-2-W3-231-2017.
• 7. I. Sa, M. Kamel, M. Burri, M. Bloesch, R. Khanna, M. Popovic, J. Nieto, and R. Siegwart, “Build Your Own Visual-Inertial Drone: A Cost-Effective and Open-Source Autonomous Drone”, IEEE Robotics & Automation Magazine, vol. 25, no. 1, pp. 89–103, 2018. http://dx.doi.org/10.1109/MRA.2017.2771326.
• 8. S. Kawabata, K. Nohara, J. H. Lee, H. Suzuki, T. Takiguchi, O. S. Park, and S. Okamoto, “Autonomous Flight Drone with Depth Camera for Inspection Task of Infra Structure”, in Proceedings of the International MultiConference of Engineers and Computer Scientists, vol. 2, 2018.
• 9. H. Sarbolandi, D. Lefloch, and A. Kolb, “Kinect Range Sensing: Structured-Light versus Time-of-Flight Kinect”, Computer vision and image understanding, vol. 139, pp. 1–20, 2015. http://dx.doi.org/10.1016/j.cviu.2015.05.006.
• 10. P. Zanuttigh, G. Marin, C. Dal Mutto, F. Dominio, L. Minto, and G. M. Cortelazzo, “Time-of-Flight and Structured Light Depth Cameras”, Technology and Applications, 2016. http://dx.doi.org/10.1007/978-3-319-30973-6.
• 11. S. T. Barnard and M. A. Fischler, “Computational Stereo”, 1982. http://dx.doi.org/10.1145/356893.356896.
• 12. T. Kanade and M. Okutomi, “A Stereo Matching Algorithm with an Adaptive Window: Theory and Experiment”, in Proceedings. 1991 IEEE International Conference on Robotics and Automation, pp. 1088–1095. http://dx.doi.org/10.1109/ROBOT.1991.131738.
• 13. L. Keselman, J. Iselin Woodfill, A. Grunnet-Jepsen, and A. Bhowmik, “Intel RealSense Stereoscopic Depth Cameras”, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2017, pp. 1–10. http://dx.doi.org/10.1109/CVPRW.2017.167.
• 14. M. Michael, J. Salmen, J. Stallkamp, and M. Schlipsing, “Real-time Stereo Vision: Optimizing Semi-Global Matching”, in IEEE Intelligent Vehicles Symposium, 2013, pp. 1197–1202. DOI : 10.1109/IVS.2013.6629629.
• 15. A. Grunnet-Jepsen and D. Tong, Depth Post-Processing for Intel RealSense D400 Depth Cameras, https://www.intel.com/content/dam/support/us/en/documents/emerging-technologies/intel-realsense-technology/Intel-RealSense-Depth-PostProcess.pdf.
• 16. H. Scharr, “Optimale Operatoren in der digitalen Bildverarbeitung”, 2000. DOI : 10.11588/heidok.00000962.
• 17. K. G. Derpanis, “The Harris Corner Detector”, York University, 2004.
• 18. A. Hornung, K. M. Wurm, M. Bennewitz, C. Stachniss, and W. Burgard, “Octomap: An Efficient Probabilistic 3D Mapping Framework Based on Octrees”, Autonomous robots, vol. 34, no. 3, pp. 189–206, 2013. DOI : 10.1007/s10514-012-9321-0.

Uwagi

1. Track 2: Computer Science & Systems

2. Technical Session: 12th International Symposium on Multimedia Applications and Processing

3. Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).