The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
Real-time onboard visual-inertial state estimation and self-calibration of MAVs in unknown environments
Open accessAbstract
The combination of visual and inertial sensors has proved to be very popular in robot navigation and, in particular, Micro Aerial Vehicle (MAV) navigation due the flexibility in weight, power consumption and low cost it offers. At the same time, coping with the big latency between inertial and visual measurements and processing images in real-time impose great research challenges. Most modern MAV navigation systems avoid to explicitly tackle this by employing a ground station for off-board processing. In this paper, we propose a navigation algorithm for MAVs equipped with a single camera and an Inertial Measurement Unit (IMU) which is able to run onboard and in real-time. The main focus here is on the proposed speed-estimation module which converts the camera into a metric body-speed sensor using IMU data within an EKF framework. We show how this module can be used for full self-calibration of the sensor suite in real-time. The module is then used both during initialization and as a fall-back solution at tracking failures of a keyframe-based VSLAM module. The latter is based on an existing high-performance algorithm, extended such that it achieves scalable 6DoF pose estimation at constant complexity. Fast onboard speed control is ensured by sole reliance on the optical flow of at least two features in two consecutive camera frames and the corresponding IMU readings. Our nonlinear observability analysis and our real experiments demonstrate that this approach can be used to control a MAV in speed, while we also show results of operation at 40Hz on an onboard Atom computer 1.6 GHz.
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Cites
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MonoSLAM: Real-time single camera SLAM
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Visual-Inertial Sensor Fusion: Localization, Mapping and Sensor-to-Sensor Self-calibration
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Autonomous multi-floor indoor navigation with a computationally constrained MAV
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Onboard IMU and monocular vision based control for MAVs in unknown in- and outdoor environments
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Self-citationImproving vision-based control using efficient second-order minimization techniques
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Real-time metric state estimation for modular vision-inertial systems
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VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator
Qin, T., Li, P., & Shen, S. (2018) · IEEE Transactions on Robotics · doi:10.1109/tro.2018.2853729 · Cited by 4,669
High-precision, consistent EKF-based visual-inertial odometry
Li, M., & Mourikis, A. I. (2013) · The International Journal of Robotics Research · doi:10.1177/0278364913481251 · Cited by 813
Li, X., Wang, X., Liao, J., Li, X., Li, S., & Lyu, H. (2021) · Satellite Navigation · doi:10.1186/s43020-020-00033-9 · Cited by 151
Advances in intelligent and autonomous navigation systems for small UAS
Bijjahalli, S., Sabatini, R., & Gardi, A. (2020) · Progress in Aerospace Sciences · doi:10.1016/j.paerosci.2020.100617 · Cited by 123
Dense RGB-D-inertial SLAM with map deformations
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Metadata
- Authors
- Weiss, S., Achtelik, M. W., Lynen, S., Chli, M., & Siegwart, R.
- Venue
- 2012 IEEE International Conference on Robotics and Automation
- Publisher
- IEEE
- Published
- 2012-05
- Pages
- 957-964
- ISSN
- 1050-4729 (ISSN-L)
- OpenAlex
- W2049750053
- Cited by
- 417
- References
- 14
- FWCI
- 72.88
- Type
- conference paper
- Language
- en
- Open access
- green
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- Weiss, S., Achtelik, M. W., Lynen, S., Chli, M., & Siegwart, R. (2012). Real-time onboard visual-inertial state estimation and self-calibration of MAVs in unknown environments. 2012 IEEE International Conference on Robotics and Automation. https://doi.org/10.1109/icra.2012.6225147
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