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Robotics Research pp 39–55Cite as

Autonomous Flights Through Image-Defined Paths

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Part of the book series: Springer Proceedings in Advanced Robotics ((SPAR,volume 2))

Abstract

This paper addresses the problem of autonomous quadrotor navigation through a previously-mapped indoor area. In particular, we focus on the case where a user walks through a building and collects images. Subsequently, a visual map of the area, represented as a graph of linked images, is constructed and used for automatically determining visual paths (i.e., sequences of images connecting the start to the end image locations specified by the user). The quadrotor follows the desired path by iteratively (i) determining the desired motion to the next reference frame, (ii) controlling its roll, pitch, yaw-rate, and thrust, and (iii) appropriately switching to a new reference image. For motion estimation and reference-image switching, we concurrently employ the results of the 2pt and the 5pt RANSAC to distinguish and deal with both cases of sufficient and insufficient baseline (e.g., rotation in place). The accuracy and robustness of our algorithm are evaluated experimentally on two quadrotors navigating along lengthy corridors, and through tight spaces inside a building and in the presence of dynamic obstacles (e.g., people walking).

This work was supported by the AFOSR (FA 9550-10-1-0567).

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Notes

  1. 1.

    The 2pt RANSAC estimates the relative orientation \(^{\scriptscriptstyle {I_{1}}}_{\scriptscriptstyle {I_{2}}}\mathbf {R}\) between two images, \(I_{\scriptscriptstyle {1}}\) and \(I_{\scriptscriptstyle {2}}\), under the assumption of very small baseline compared to the depth of the scene. A closed-form solution for the 2pt minimal case is provided in Sect. 6.2, while the analytical solution for the least-squares solver is presented in [21]. The 5pt RANSAC [26] estimates the relative orientation \(^{\scriptscriptstyle {I_{1}}}_{\scriptscriptstyle {I_{2}}}\mathbf {R}\) and the unit vector of translation \(^{\scriptscriptstyle {I_{1}}}\mathbf {t}_{\scriptscriptstyle {I_{2}}}\) between two images \(I_{\scriptscriptstyle {1}}\) and \(I_{\scriptscriptstyle {2}}\).

  2. 2.

    Under low-light conditions, the velocity measurements are reliable only for a fixed tilt angle of the vehicle. Note that when in motion, the quadrotor changes its roll and pitch which causes image blurriness (due to the increased exposure) and, hence, large errors in the optical-flow estimates.

  3. 3.

    Note that although both the embedded controller and the cell phone contain IMUs, which can be used, in conjunction with the camera, to form a vision-aided inertial navigation system [18], in this work, we intentionally focus on a “light”, in terms of processing, vision-only approach so as to assess its performance and use it as a baseline for future comparisons.

  4. 4.

    Note that since all images were recorded at about the same height, the z component of the desired motion estimate is rather small after the first reference image and we subsequently ignore it. Instead, we use the distance-to-the-ground measurements to maintain a constant-altitude flight.

  5. 5.

    This threshold depends on the onboard camera’s fov and is selected so as to ensure a significant overlap (more than 80%) between the current camera image and the next reference image.

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Authors and Affiliations

  1. University of Minnesota, Minneapolis, MN, 55455, USA

    Tien Do, Luis C. Carrillo-Arce & Stergios I. Roumeliotis

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  1. Tien Do
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  2. Luis C. Carrillo-Arce
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  3. Stergios I. Roumeliotis
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Correspondence to Tien Do .

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Editors and Affiliations

  1. Istituto Italiano di Tecnologia, Genova, Italy, University of Pisa, Pisa, Italy , Pisa, Italy

    Antonio Bicchi

  2. Inst. für Informatik, Albert-Ludwigs-Universität Freiburg Inst. für Informatik, Freiburg, Germany

    Wolfram Burgard

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Do, T., Carrillo-Arce, L.C., Roumeliotis, S.I. (2018). Autonomous Flights Through Image-Defined Paths. In: Bicchi, A., Burgard, W. (eds) Robotics Research. Springer Proceedings in Advanced Robotics, vol 2. Springer, Cham. https://doi.org/10.1007/978-3-319-51532-8_3

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  • DOI: https://doi.org/10.1007/978-3-319-51532-8_3

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