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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. 

Miniature autonomous blimps are autonomous lighter-than-air vehicles that offer a variety of benefits over other existing flight platforms. In particular, blimps offer long flight times, soft envelopes that are resilient to collisions, and friendly human-robot interaction opportunities. As such, these platforms are well suited for indoor applications and human-cluttered environments as catastrophic or life-threatening collisions are far less likely. In this abstract, we detail some of our ongoing efforts to enable autonomous behaviors for lighter-than-air platforms through various sensing, actuation, and swarming efforts.