The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Autonomous, Vision‐based Flight and Live Dense 3D Mapping with a Quadrotor Micro Aerial Vehicle
Open accessAbstract
The use of mobile robots in search‐and‐rescue and disaster‐response missions has increased significantly in recent years. However, they are still remotely controlled by expert professionals on an actuator set‐point level, and they would benefit, therefore, from any bit of autonomy added. This would allow them to execute high‐level commands, such as “execute this trajectory” or “map this area.” In this paper, we describe a vision‐based quadrotor micro aerial vehicle that can autonomously execute a given trajectory and provide a live, dense three‐dimensional (3D) map of an area. This map is presented to the operator while the quadrotor is mapping, so that there are no unnecessary delays in the mission. Our system does not rely on any external positioning system (e.g., GPS or motion capture systems) as sensing, computation, and control are performed fully onboard a smartphone processor. Since we use standard, off‐the‐shelf components from the hobbyist and smartphone markets, the total cost of our system is very low. Due to its low weight (below 450 g), it is also passively safe and can be deployed close to humans. We describe both the hardware and the software architecture of our system. We detail our visual odometry pipeline, the state estimation and control, and our live dense 3D mapping, with an overview of how all the modules work and how they have been integrated into the final system. We report the results of our experiments both indoors and outdoors. Our quadrotor was demonstrated over 100 times at multiple trade fairs, at public events, and to rescue professionals. We discuss the practical challenges and lessons learned. Code, datasets, and videos are publicly available to the robotics community.
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Cites
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
Bundle Adjustment — A Modern Synthesis
Triggs, B., McLauchlan, P. F., Hartley, R. I., & Fitzgibbon, A. W. (2000) · Lecture Notes in Computer Science · doi:10.1007/3-540-44480-7_21 · Cited by 3,988
DTAM: Dense tracking and mapping in real-time
Newcombe, R. A., Lovegrove, S. J., & Davison, A. J. (2011) · 2011 International Conference on Computer Vision · doi:10.1109/iccv.2011.6126513 · Cited by 2,078 · cited 3×
“We pe-nalize nonsmooth surfaces by making use of a regularization term based on the Huber norm of the gradient, defined as We chose the Huber norm because it allows smooth reconstruction while preserving discontinuities at strong depth gradient locations (Newcombe et al., 2011).”
MethodInfluentialScaramuzza, D., & Fraundorfer, F. (2011) · IEEE Robotics & Automation Magazine · doi:10.1109/mra.2011.943233 · Cited by 1,550
Self-citationMonocular‐SLAM–based navigation for autonomous micro helicopters in GPS‐denied environments
Weiss, S., Scaramuzza, D., & Siegwart, R. (2011) · Journal of Field Robotics · doi:10.1002/rob.20412 · Cited by 414
Self-citationREMODE: Probabilistic, monocular dense reconstruction in real time
Pizzoli, M., Forster, C., & Scaramuzza, D. (2014) · 2014 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2014.6907233 · Cited by 330 · cited 5×
“Therefore, we use the REMODE (“Regularized Monocular Depth”) algorithm that we proposed in Pizzoli et al. (2014) and that we summarize in the following.”
MethodInfluentialSelf-citationIrani, M., & Anandan, P. (2000) · Lecture Notes in Computer Science · doi:10.1007/3-540-44480-7_18 · Cited by 250
Cited by
A Machine Learning Approach to Visual Perception of Forest Trails for Mobile Robots
Giusti, A., Guzzi, J., Ciresan, D. C., He, F.-L., Rodriguez, J. P., Fontana, F., Faessler, M., Forster, C., Schmidhuber, J., Caro, G. D., et al. (2016) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2015.2509024 · Cited by 665
Self-citationLearning high-speed flight in the wild
Loquercio, A., Kaufmann, E., Ranftl, R., Müller, M., Koltun, V., & Scaramuzza, D. (2021) · Science Robotics · doi:10.1126/scirobotics.abg5810 · Cited by 375
Self-citationThe current state and future outlook of rescue robotics
Delmerico, J., Mintchev, S., Giusti, A., Gromov, B., Melo, K., Horvat, T., Cadena, C., Hutter, M., Ijspeert, A., Floreano, D., et al. (2019) · Journal of Field Robotics · doi:10.1002/rob.21887 · Cited by 371
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Metadata
- Authors
- Faessler, M., Fontana, F., Forster, C., Mueggler, E., Pizzoli, M., & Scaramuzza, D.
- Venue
- Journal of Field Robotics
- Publisher
- Wiley
- Published
- 2015-03-17
- Volume
- 33
- Issue
- 4
- Pages
- 431-450
- ISSN
- 1556-4959 (ISSN-L)
- 1556-4967
- OpenAlex
- W1530919911
- Cited by
- 247
- References
- 7
- FWCI
- 40.36
- Type
- journal article
- Language
- en
- Open access
- green
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