The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
Vision-based autonomous mapping and exploration using a quadrotor MAV
Abstract
In this paper, we describe our autonomous vision-based quadrotor MAV system which maps and explores unknown environments. All algorithms necessary for autonomous mapping and exploration run on-board the MAV. Using a front-looking stereo camera as the main exteroceptive sensor, our quadrotor achieves these capabilities with both the Vector Field Histogram+ (VFH+) algorithm for local navigation, and the frontier-based exploration algorithm. In addition, we implement the Bug algorithm for autonomous wall-following which could optionally be selected as the substitute exploration algorithm in sparse environments where the frontier-based exploration under-performs. We incrementally build a 3D global occupancy map on-board the MAV. The map is used by the VFH+ and frontier-based exploration in dense environments, and the Bug algorithm for wall-following in sparse environments. During the exploration phase, images from the front-looking camera are transmitted over Wi-Fi to the ground station. These images are input to a large-scale visual SLAM process running off-board on the ground station. SLAM is carried out with pose-graph optimization and loop closure detection using a vocabulary tree. We improve the robustness of the pose estimation by fusing optical flow and visual odometry. Optical flow data is provided by a customized downward-looking camera integrated with a microcontroller while visual odometry measurements are derived from the front-looking stereo camera. We verify our approaches with experimental results.
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Cites
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BRIEF: Binary Robust Independent Elementary Features
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Scalable Recognition with a Vocabulary Tree
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A frontier-based approach for autonomous exploration
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Cited by
Learning deep control policies for autonomous aerial vehicles with MPC-guided policy search
Zhang, T., Kahn, G., Levine, S., & Abbeel, P. (2016) · 2016 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2016.7487175 · Cited by 452
“This challenge is conventionally addressed by using localization and mapping algorithms to map out the environment [9] and then optimizing trajectories under the resulting map [11].”
A UAV system for inspection of industrial facilities
Nikolic, J., Burri, M., Rehder, J., Leutenegger, S., Huerzeler, C., & Siegwart, R. (2013) · 2013 IEEE Aerospace Conference · doi:10.1109/aero.2013.6496959 · Cited by 251
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
Metadata
- Authors
- Fraundorfer, F., Heng, L., Honegger, D., Lee, G. H., Meier, L., Tanskanen, P., & Pollefeys, M.
- Venue
- 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems
- Publisher
- IEEE
- Published
- 2012-10
- Pages
- 4557-4564
- OpenAlex
- W2087387266
- Cited by
- 313
- References
- 23
- FWCI
- 54.36
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Fraundorfer, F., Heng, L., Honegger, D., Lee, G. H., Meier, L., Tanskanen, P., & Pollefeys, M. (2012). Vision-based autonomous mapping and exploration using a quadrotor MAV. 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems. https://doi.org/10.1109/iros.2012.6385934
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