An official website of the United States government
Human remote-control (RC) pilots have the ability to perceive the position and orientation of an aircraft using only third-person-perspective visual sensing. While novice pilots often struggle when learning to control RC aircraft, they can sense the orientation of the aircraft with relative ease. In this paper, we hypothesize and demonstrate that deep learning methods can be used to mimic the human ability to perceive the orientation of an aircraft from monocular imagery. This work uses a neural network to directly sense the aircraft attitude. The network is combined with more conventional image processing methods for visual tracking of the aircraft. The aircraft track and attitude measurements from the convolutional neural network (CNN) are combined in a particle filter that provides a complete state estimate of the aircraft. The network topology, training, and testing results are presented as well as filter development and results. The proposed method was tested in simulation and hardware flight demonstrations.
more »
« less
Deep Visual Gravity Vector Detection for Unmanned Aircraft Attitude Estimation
This paper demonstrates a feasible method for using a deep neural network as a sensor to estimate the attitude of a flying vehicle using only flight video. A dataset of still images and associated gravity vectors was collected and used to perform supervised learning. The network builds on a previously trained network and was trained to be able to approximate the attitude of the camera with an average error of about 8 degrees. Flight test video was recorded and processed with a relatively simple visual odometry method. The aircraft attitude is then estimated with the visual odometry as the state propagation and network providing the attitude measurement in an extended Kalman filter. Results show that the proposed method of having the neural network provide a gravity vector attitude measurement from the flight imagery reduces the standard deviation of the attitude error by approximately 12 times compared to a baseline approach.
more »
« less
- Award ID(s):
- 1650547
- PAR ID:
- 10053362
- Date Published:
- Journal Name:
- IEEE/RSJ International Conference on Intelligent Robots and Systems
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract Global optical flow estimation is the foundation stone for obtaining odometry which is used to enable aerial robot navigation. However, such a method has to be of low latency and high robustness whilst also respecting the size, weight, area and power (SWAP) constraints of the robot. A combination of cameras coupled with inertial measurement units (IMUs) has proven to be the best combination in order to obtain such low latency odometry on resource‐constrained aerial robots. Recently, deep learning approaches for visual inertial fusion have gained momentum due to their high accuracy and robustness. However, an equally noteworthy benefit for robotics of these techniques are their inherent scalability (adaptation to different sized aerial robots) and unification (same method works on different sized aerial robots). To this end, we present a deep learning approach called PRGFlow for obtaining global optical flow and then loosely fuse it with an IMU for full 6‐DoF (Degrees of Freedom) relative pose estimation (which is then integrated to obtain odometry). The network is evaluated on the MSCOCO dataset and the dead‐reckoned odometry on multiple real‐flight trajectories without any fine‐tuning or re‐training. A detailed benchmark comparing different network architectures and loss functions to enable scalability is also presented. It is shown that the method outperforms classical feature matching methods by 2 under noisy data. The supplementary material and code can be found athttp://prg.cs.umd.edu/PRGFlow.more » « less
-
Unsupervised visual odometry as an active topic has attracted extensive attention, benefiting from its label-free practical value and robustness in real-world scenarios. However, the performance of camera pose estimation and tracking through deep neural network is still not as ideal as most other tasks, such as detection, segmentation and depth estimation, due to the lack of drift correction in the estimated trajectory and map optimization in the recovered 3D scenes. In this work, we introduce pose graph and bundle adjustment optimization to our network training process, which iteratively updates both the motion and depth estimations from the deep learning network, and enforces the refined outputs to further meet the unsupervised photometric and geometric constraints. The integration of pose graph and bundle adjustment is easy to implement and significantly enhances the training effectiveness. Experiments on KITTI dataset demonstrate that the introduced method achieves a significant improvement in motion estimation compared with other recent unsupervised monocular visual odometry algorithms.more » « less
-
Unsupervised visual odometry as an active topic has attracted extensive attention, benefiting from its label free practical value and robustness in real-world scenarios. However, the performance of camera pose estimation and tracking through deep neural network is still not as ideal as most other tasks, such as detection, segmentation and depth estimation, due to the lack of drift correction in the estimated trajectory and map optimization in the recovered 3D scenes. In this work, we introduce pose graph and bundle adjustment optimization to our network training process, which iteratively updates both the motion and depth estimations from the deep learning network, and enforces the refined outputs to further meet the unsupervised photometric and geometric constraints. The integration of pose graph and bundle adjustment is easy to implement and significantly enhances the training effectiveness. Experiments on KITTI dataset demonstrate that the introduced method achieves a significant improvement in motion estimation compared with other recent unsupervised monocular visual odometry algorithms.more » « less
-
This work presents a multiplicative extended Kalman filter (MEKF) for estimating the relative state of a multirotor vehicle operating in a GPS-denied environment. The filter fuses data from an inertial measurement unit and altimeter with relative-pose updates from a keyframe-based visual odometry or laser scan-matching algorithm. Because the global position and heading states of the vehicle are unobservable in the absence of global measurements such as GPS, the filter in this article estimates the state with respect to a local frame that is colocated with the odometry keyframe. As a result, the odometry update provides nearly direct measurements of the relative vehicle pose, making those states observable. Recent publications have rigorously documented the theoretical advantages of such an observable parameterization, including improved consistency, accuracy, and system robustness, and have demonstrated the effectiveness of such an approach during prolonged multirotor flight tests. This article complements this prior work by providing a complete, self-contained, tutorial derivation of the relative MEKF, which has been thoroughly motivated but only briefly described to date. This article presents several improvements and extensions to the filter while clearly defining all quaternion conventions and properties used, including several new useful properties relating to error quaternions and their Euler-angle decomposition. Finally, this article derives the filter both for traditional dynamics defined with respect to an inertial frame, and for robocentric dynamics defined with respect to the vehicle’s body frame, and provides insights into the subtle differences that arise between the two formulations.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
