A UAV system for inspection of industrial facilities
Open accessAbstract
This work presents a small-scale Unmanned Aerial System (UAS) capable of performing inspection tasks in enclosed industrial environments. Vehicles with such capabilities have the potential to reduce human involvement in hazardous tasks and can minimize facility outage periods. The results presented generalize to UAS exploration tasks in almost any GPS-denied indoor environment. The contribution of this work is twofold. First, results from autonomous flights inside an industrial boiler of a power plant are presented. A lightweight, vision-aided inertial navigation system provides reliable state estimates under difficult environmental conditions typical for such sites. It relies solely on measurements from an on-board MEMS inertial measurement unit and a pair of cameras arranged in a classical stereo configuration. A model-predictive controller allows for efficient trajectory following and enables flight in close proximity to the boiler surface. As a second contribution, we highlight ongoing developments by displaying state estimation and structure recovery results acquired with an integrated visual/inertial sensor that will be employed on future aerial service robotic platforms. A tight integration in hardware facilitates spatial and temporal calibration of the different sensors and thus enables more accurate and robust ego-motion estimates. Comparison with ground truth obtained from a laser tracker shows that such a sensor can provide motion estimates with drift rates of only few centimeters over the period of a typical flight.
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Ethereum is making steady progress towards scalability with on-chain sharding and off-chain Raiden and Plasma.
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Cites
Machine Learning for High-Speed Corner Detection
Rosten, E., & Drummond, T. (2006) · Lecture Notes in Computer Science · doi:10.1007/11744023_34 · Cited by 4,555 · see citing works
Parallel Tracking and Mapping for Small AR Workspaces
Klein, G., & Murray, D. (2007) · 2007 6th IEEE and ACM International Symposium on Mixed and Augmented Reality · doi:10.1109/ismar.2007.4538852 · Cited by 4,346 · see citing works
BRIEF: Binary Robust Independent Elementary Features
Calonder, M., Lepetit, V., Strecha, C., & Fua, P. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15561-1_56 · Cited by 3,671 · see citing works
BRISK: Binary Robust invariant scalable keypoints
Leutenegger, S., Chli, M., & Siegwart, R. (2011) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2011.6126542 · Cited by 3,551 · see citing works
Self-citationUnified temporal and spatial calibration for multi-sensor systems
Furgale, P., Rehder, J., & Siegwart, R. (2013) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2013.6696514 · Cited by 834 · see citing works
Self-citationVisual Odometry and Mapping for Autonomous Flight Using an RGB-D Camera
Huang, A. S., Bachrach, A., Henry, P., Krainin, M., Maturana, D., Fox, D., & Roy, N. (2016) · Springer tracts in advanced robotics · doi:10.1007/978-3-319-29363-9_14 · Cited by 607 · see citing works
Visual-Inertial Sensor Fusion: Localization, Mapping and Sensor-to-Sensor Self-calibration
Kelly, J. W., & Sukhatme, G. S. (2010) · The International Journal of Robotics Research · doi:10.1177/0278364910382802 · Cited by 537 · see citing works
Real-time monocular SLAM: Why filter?
Strasdat, H., Montiel, J. M. M., & Davison, A. J. (2010) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2010.5509636 · Cited by 485 · see citing works
Mirzaei, F. M., & Roumeliotis, S. I. (2008) · IEEE Transactions on Robotics · doi:10.1109/tro.2008.2004486 · Cited by 446 · see citing works
Vision based MAV navigation in unknown and unstructured environments
Bloesch, M., Weiß, S., Scaramuzza, D., & Siegwart, R. (2010) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2010.5509920 · Cited by 423 · see citing works
Self-citationAutonomous multi-floor indoor navigation with a computationally constrained MAV
Shen, S., Michael, N., & Kumar, V. (2011) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2011.5980357 · Cited by 360 · see citing works
Vision-based autonomous mapping and exploration using a quadrotor MAV
Fraundorfer, F., Heng, L., Honegger, D., Lee, G. H., Meier, L., Tanskanen, P., & Pollefeys, M. (2012) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2012.6385934 · Cited by 319 · see citing works
FPGA Design and Implementation of a Real-Time Stereo Vision System
Jin, S., Cho, J., Pham, X. D., et al. (2009) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2009.2026831 · Cited by 319 · see citing works
Continuous-time batch estimation using temporal basis functions
Furgale, P., Barfoot, T. D., & Sibley, G. (2012) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2012.6225005 · Cited by 265 · see citing works
Real-time metric state estimation for modular vision-inertial systems
Weiß, S., & Siegwart, R. (2011) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2011.5979982 · Cited by 221 · see citing works
Self-citationAutonomous Flight in Unknown Indoor Environments
Bachrach, A., He, R., & Roy, N. (2009) · International Journal of Micro Air Vehicles · doi:10.1260/175682909790291492 · Cited by 205 · see citing works
Motion Estimation from Image and Inertial Measurements
Strelow, D., & Singh, S. (2004) · The International Journal of Robotics Research · doi:10.1177/0278364904045593 · Cited by 151 · see citing works
Aerial service robots for visual inspection of thermal power plant boiler systems
Burri, M., Nikolic, J., Hürzeler, C., Caprari, G., & Siegwart, R. (2012) · International Conference on Applied Robotics for the Power Industry (CARPI) · doi:10.1109/carpi.2012.6473374 · Cited by 86 · see citing works
Self-citationA General Framework for Temporal Calibration of Multiple Proprioceptive and Exteroceptive Sensors
Kelly, J. W., & Sukhatme, G. S. (2013) · Springer tracts in advanced robotics · doi:10.1007/978-3-642-28572-1_14 · Cited by 84 · see citing works
Robust embedded egomotion estimation
Voigt, R., Nikolic, J., Hürzeler, C., Weiß, S., Kneip, L., & Siegwart, R. (2011) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2011.6095122 · Cited by 35 · see citing works
Self-citationOn the treatment of relative-pose measurements for mobile robot localization
Mourikis, A. I., & Roumeliotis, S. I. (2006) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2006.1642042 · Cited by 29 · see citing works
Tensor-voting-based navigation for robotic inspection of 3D surfaces using lidar point clouds
Stumm, E., Breitenmoser, A., Pomerleau, F., et al. (2012) · The International Journal of Robotics Research · doi:10.1177/0278364912461537 · Cited by 28 · see citing works
GIMME - A General Image Multiview Manipulation Engine
Ahlberg, C., Lidholm, J., Ekstrand, F., et al. (2011) · International Conference on ReConFigurable Computing and FPGAs (ReConFig) · doi:10.1109/reconfig.2011.44 · Cited by 15 · see citing works
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Survey of computer vision algorithms and applications for unmanned aerial vehicles
Al-Kaff, A., Martín, D., García, F., Escalera, A. D. L., & María Armingol, J. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.09.033 · Cited by 246 · see citing works
- Authors
- Nikolic, J., Burri, M., Rehder, J., Leutenegger, S., Huerzeler, C., & Siegwart, R.
- Affiliations
- ETH Zurich (CH)
- Venue
- 2013 IEEE Aerospace Conference
- Publisher
- IEEE
- Published
- 2013-03
- Pages
- 1-8
- ISSN
- 1095-323X (ISSN-L)
- OpenAlex
- W2006433700
- Cited by
- 251
- References
- 23
- Citations by year
- 2026: 10 · 2025: 16 · 2024: 24 · 2023: 24 · 2022: 26 · 2021: 33 · 2020: 25 · 2019: 16 · 2018: 23 · 2017: 15 · 2016: 9 · 2015: 13 · 2014: 12 · 2013: 5
- FWCI
- 22.95
- Type
- conference paper
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- other-oa
- Locations
- IEEE Aerospace Conference (AERO) (publishedVersion)
- Repository for Publications and Research Data (ETH Zurich) (submittedVersion, other-oa)
- ETH Zürich Research Collection
- Topics
- Physical Sciences › Engineering › Aerospace Engineering › Robotics and Sensor-Based Localization
- Physical Sciences › Engineering › Aerospace Engineering › Aerospace Engineering and Control Systems
- Physical Sciences › Engineering › Aerospace Engineering › Inertial Sensor and Navigation
- Keywords
- unmanned aerial systems, indoor exploration, GPS-denied environments, vision-aided inertial navigation, stereo vision, MEMS inertial measurement unit, model predictive control, trajectory tracking, power plant boilers, sensor calibration, state estimation, structure recovery
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