Monocular‐SLAM–based navigation for autonomous micro helicopters in GPS‐denied environments
Abstract
Autonomous micro aerial vehicles (MAVs) will soon play a major role in tasks such as search and rescue, environment monitoring, surveillance, and inspection. They allow us to easily access environments to which no humans or other vehicles can get access. This reduces the risk for both the people and the environment. For the above applications, it is, however, a requirement that the vehicle is able to navigate without using GPS, or without relying on a preexisting map, or without specific assumptions about the environment. This will allow operations in unstructured, unknown, and GPS‐denied environments. We present a novel solution for the task of autonomous navigation of a micro helicopter through a completely unknown environment by using solely a single camera and inertial sensors onboard. Many existing solutions suffer from the problem of drift in the xy plane or from the dependency on a clean GPS signal. The novelty in the here‐presented approach is to use a monocular simultaneous localization and mapping (SLAM) framework to stabilize the vehicle in six degrees of freedom. This way, we overcome the problem of both the drift and the GPS dependency. The pose estimated by the visual SLAM algorithm is used in a linear optimal controller that allows us to perform all basic maneuvers such as hovering, set point and trajectory following, vertical takeoff, and landing. All calculations including SLAM and controller are running in real time and online while the helicopter is flying. No offline processing or preprocessing is done. We show real experiments that demonstrate that the vehicle can fly autonomously in an unknown and unstructured environment. To the best of our knowledge, the here‐presented work describes the first aerial vehicle that uses onboard monocular vision as a main sensor to navigate through an unknown GPS‐denied environment and independently of any external artificial aids. © 2011 Wiley Periodicals, Inc.
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The Table 2 results for VIOLearner, UnDeepVO, and SFMLearner come from networks tested on data that was also used for training.
“It should be noted that the results in Table 2 for VIOLearner, UnDeepVO, and SFMLearner are for networks that were tested on data on which they were also trained which is in accordance with the results presented in [35].”
From Unsupervised Deep Visual-Inertial Odometry with Online Error Correction for RGB-D Imagery (2020) · cites this paper as [35]
UnDeepVO is an unsupervised depth and egomotion method that produces properly scaled trajectories, but it uses stereo image pairs for training and cannot be extended to inertial measurements because stereo spatial transformations are unobservable by an IMU.
“Additionally, the network architecture of UnDeepVO cannot be extended to include motion estimates derived from inertial measurements because the spatial transformation between paired images from stereo cameras are unobservable by an IMU (stereo images are recorded simultaneously).”
From Unsupervised Deep Visual-Inertial Odometry with Online Error Correction for RGB-D Imagery (2020) · cites this paper as [35]
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- Authors
- Weiss, S., Scaramuzza, D., & Siegwart, R.
- Affiliations
- ETH Zurich (CH)
- University of Pennsylvania (US)
- Venue
- Journal of Field Robotics
- Publisher
- Wiley
- Published
- 2011-10-12
- Volume
- 28
- Issue
- 6
- Pages
- 854-874
- ISSN
- 1556-4959 (ISSN-L)
- 1556-4967
- OpenAlex
- W2132453268
- Cited by
- 414
- References
- 59
- Citations by year
- 2026: 11 · 2025: 9 · 2024: 13 · 2023: 11 · 2022: 11 · 2021: 20 · 2020: 29 · 2019: 37 · 2018: 46 · 2017: 47 · 2016: 33 · 2015: 46 · 2014: 32 · 2013: 46 · 2012: 21
- FWCI
- 57.55
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Field Robotics (publishedVersion)
- Funders
- Eidgenössische Technische Hochschule Zürich
- 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
- monocular SLAM, GPS-denied navigation, visual SLAM, inertial sensors, linear optimal control, trajectory tracking, hover, vertical takeoff and landing, unknown environments, micro air vehicles
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