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This paper proposes a nudged particle filter for estimating the pose of a camera mounted on flying robots collecting a video sequence. The nudged particle filter leverages two image-to-pose and pose-to-image neural networks trained in an auto-encoder fashion with a dataset of pose-labeled images. Given an image, the retrieved camera pose using the image-to-pose network serves as a special particle to nudge the set of particles generated from the particle filter while the pose-to-image network serves to compute the likelihoods of each particle. We demonstrate that such a nudging scheme effectively mitigates low likelihood samplings during the particle propagation step. Ellipsoidal confidence tubes are constructed from the set of particles to provide a computationally efficient bound on localization error. When an ellipsoidal tube self-intersects, the probability volume of the intersection can be significantly shrunken using a novel Dempster–Shafer probability mass assignment algorithm. Starting from the intersection, a loop closure procedure is developed to move backward in time to shrink the volumes of the entire ellipsoidal tube. Experimental results using the Georgia Tech Miniature Autonomous Blimp platform are provided to demonstrate the feasibility and effectiveness of the proposed algorithms in providing localization and pose estimation based on monocular vision.
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Set-Based State Estimation of Mobile Robots from Coarse Range Measurements
This paper proposes a localization algorithm for an autonomous mobile robot equipped with binary proximity sensors that only indicate when the robot is within a fixed distance from beacons installed at known positions. Our algorithm leverages an ellipsoidal Set Membership State Estimator (SMSE) that maintains an ellipsoidal bound of the position and velocity states of the robot. The estimate incorporates knowledge of the robot's dynamics, bounds on environmental disturbances, and the binary sensor readings. The localization algorithm is motivated by an underwater scenario where accurate range or bearing measurements are often missing. We demonstrate our approach on an experimental platform using an autonomous blimp.
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- PAR ID:
- 10212084
- Date Published:
- Journal Name:
- Proceedings of 4th IEEE Conference on Control Technology and Applications
- Page Range / eLocation ID:
- 404 to 409
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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null (Ed.)The Georgia Tech Miniature Autonomous Blimp (GT-MAB) needs localization algorithms to navigate to way-points in an indoor environment without leveraging an external motion capture system. Indoor aerial robots often require a motion capture system for localization or employ simultaneous localization and mapping (SLAM) algorithms for navigation. The proposed strategy for GT-MAB localization can be accomplished using lightweight sensors on a weight-constrained platform like the GT-MAB. We train an end-to-end convolutional neural network (CNN) that predicts the horizontal position and heading of the GT-MAB using video collected by an onboard monocular RGB camera. On the other hand, the height of the GT-MAB is estimated from measurements through a time-of-flight (ToF) single-beam laser sensor. The monocular camera and the single-beam laser sensor are sufficient for the localization algorithm to localize the GT-MAB in real time, achieving the averaged 3D positioning errors to be less than 20 cm, and the averaged heading errors to be less than 3 degrees. With the accuracy of our proposed localization method, we are able to use simple proportional-integral-derivative controllers to control the GT-MAB for waypoint navigation. Experimental results on the waypoint following are provided, which demonstrates the use of a CNN as the primary localization method for estimating the pose of an indoor robot that successfully enables navigation to specified waypoints.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/CCTA41146.2020.9206321
