Learned Inertial Odometry for Autonomous Drone Racing Article Swipe
Giovanni Cioffi
,
Leonard Bauersfeld
,
Elia Kaufmann
,
Davide Scaramuzza
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.5167/uzh-257370
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.5167/uzh-257370
Inertial odometry is an attractive solution to the problem of state estimation for agile quadrotor flight. It is inexpensive, lightweight, and it is not affected by perceptual degradation. However, only relying on the integration of the inertial measurements for state estimation is infeasible. The errors and time-varying biases present in such measurements cause the accumulation of large drift in the pose estimates. Recently, inertial odometry has made significant progress in estimating the motion of pedestrians. State-of-the-art algorithms rely on learning a motion prior that is typical of humans but cannot be transferred to drones. In this work, we propose a learning-based odometry algorithm that uses an inertial measurement unit (IMU) as the only sensor modality for autonomous drone racing tasks. The core idea of our system is to couple a model-based filter, driven by the inertial measurements, with a learning-based module that has access to the thrust measurements. We show that our inertial odometry algorithm is superior to the state-of-the-art filter-based and optimization-based visual-inertial odometry as well as the state-of-the-art learned-inertial odometry in estimating the pose of an autonomous racing drone. Additionally, we show that our system is comparable to a visual-inertial odometry solution that uses a camera and exploits the known gate location and appearance. We believe that the application in autonomous drone racing paves the way for novel research in inertial odometry for agile quadrotor flight.
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Metadata
- Type
- preprint
- Language
- en
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4307536604
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4307536604Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.5167/uzh-257370Digital Object Identifier
- Title
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Learned Inertial Odometry for Autonomous Drone RacingWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-05-01Full publication date if available
- Authors
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Giovanni Cioffi, Leonard Bauersfeld, Elia Kaufmann, Davide ScaramuzzaList of authors in order
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- Concepts
-
Odometry, Inertial measurement unit, Artificial intelligence, Computer vision, Computer science, Inertial frame of reference, Visual odometry, Kalman filter, Robot, Mobile robot, Physics, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.Inertial | 0 |
| abstract_inverted_index.affected | 24 |
| abstract_inverted_index.exploits | 199 |
| abstract_inverted_index.inertial | 36, 63, 106, 135, 152, 222 |
| abstract_inverted_index.learning | 79 |
| abstract_inverted_index.location | 203 |
| abstract_inverted_index.modality | 114 |
| abstract_inverted_index.odometry | 1, 64, 101, 153, 164, 171, 192, 223 |
| abstract_inverted_index.progress | 68 |
| abstract_inverted_index.research | 220 |
| abstract_inverted_index.solution | 5, 193 |
| abstract_inverted_index.superior | 156 |
| abstract_inverted_index.Recently, | 62 |
| abstract_inverted_index.algorithm | 102, 154 |
| abstract_inverted_index.quadrotor | 14, 226 |
| abstract_inverted_index.algorithms | 76 |
| abstract_inverted_index.attractive | 4 |
| abstract_inverted_index.autonomous | 116, 178, 212 |
| abstract_inverted_index.comparable | 188 |
| abstract_inverted_index.estimates. | 61 |
| abstract_inverted_index.estimating | 70, 173 |
| abstract_inverted_index.estimation | 11, 40 |
| abstract_inverted_index.perceptual | 26 |
| abstract_inverted_index.appearance. | 205 |
| abstract_inverted_index.application | 210 |
| abstract_inverted_index.infeasible. | 42 |
| abstract_inverted_index.integration | 33 |
| abstract_inverted_index.measurement | 107 |
| abstract_inverted_index.model-based | 130 |
| abstract_inverted_index.significant | 67 |
| abstract_inverted_index.transferred | 91 |
| abstract_inverted_index.accumulation | 54 |
| abstract_inverted_index.degradation. | 27 |
| abstract_inverted_index.filter-based | 160 |
| abstract_inverted_index.inexpensive, | 18 |
| abstract_inverted_index.lightweight, | 19 |
| abstract_inverted_index.measurements | 37, 51 |
| abstract_inverted_index.pedestrians. | 74 |
| abstract_inverted_index.time-varying | 46 |
| abstract_inverted_index.Additionally, | 181 |
| abstract_inverted_index.measurements, | 136 |
| abstract_inverted_index.measurements. | 147 |
| abstract_inverted_index.learning-based | 100, 139 |
| abstract_inverted_index.visual-inertial | 163, 191 |
| abstract_inverted_index.State-of-the-art | 75 |
| abstract_inverted_index.learned-inertial | 170 |
| abstract_inverted_index.state-of-the-art | 159, 169 |
| abstract_inverted_index.optimization-based | 162 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.00236952 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |