Abstract
Vision-based localization systems rely on highly-textured areas for achieving an accurate pose estimation. However, most previous path planning strategies propose to select trajectories with minimum pose uncertainty by leveraging only the geometric structure of the scene, neglecting the photometric information (i.e, texture). Our planner exploits the scene’s visual appearance (i.e, the photometric information) in combination with its 3D geometry. Furthermore, we assume that we have no prior knowledge about the environment given, meaning that there is no pre-computed map or 3D geometry available. We introduce a novel approach to update the optimal plan on-the-fly, as new visual information is gathered. We demonstrate our approach with real and simulated Micro Aerial Vehicles (MAVs) that perform perception-aware path planning in real-time during exploration. We show significantly reduced pose uncertainty over trajectories planned without considering the perception of the robot.
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References
Engel, J., Schöps, T., Cremers, D.: LSD-SLAM: large-scale direct monocular SLAM. In: European Conference on Computer Vision (ECCV), pp. 834–849 (2014)
Forster, C., Pizzoli, M., Scaramuzza, M.: SVO: fast semi-direct monocular visual odometry. In: IEEE International Conference on Robotics and Automation (ICRA) (2014)
Weiss, S., Achtelik, M.W., Lynen, S., Achtelik, M.C., Kneip, L., Chli, M., Siegwart, R.: Monocular vision for long-term micro aerial vehicle state estimation: a compendium. J. Field Robot. 30(5), 803–831 (2013)
Scaramuzza, D., Achtelik, M.C., Doitsidis, L., Fraundorfer, F., Kosmatopoulos, E.B., Martinelli, A., Achtelik, M.W., Chli, M., Chatzichristofis, S.A., Kneip, L., Gurdan, D., Heng, L., Lee, G.H., Lynen, S., Meier, L., Pollefeys, M., Renzaglia, A., Siegwart, R., Stumpf, J.C., Tanskanen, P., Troiani, C., Weiss, S.: Vision-controlled micro flying robots: from system design to autonomous navigation and mapping in GPS-denied environments. IEEE Robot. Autom. Mag. 21, 26–40 (2014)
Grzonka, S., Grisetti, G., Burgard, W.: A fully autonomous indoor quadrotor. IEEE Trans. Robot. 28(1), 90–100 (2012)
Nieuwenhuisen, M., Droeschel, D., Beul, M., Behnke, S.: Obstacle detection and navigation planning for autonomous micro aerial vehicles. In: 2014 International Conference on Unmanned Aircraft Systems (ICUAS), pp. 1040–1047. IEEE (2014)
Bry, A., Roy, N.: Rapidly-exploring random belief trees for motion planning under uncertainty. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 723–730 (2011)
Bachrach, A., Prentice, S., He, R., Henry, P., Huang, A.S., Krainin, M., Maturana, D., Fox, D., Roy, N.: Estimation, planning, and mapping for autonomous flight using an RGB-D camera in GPS-denied environments. Int. J. Robot. Res. 31(11), 1320–1343 (2012)
Sim, R., Roy, N.: Global a-optimal robot exploration in slam. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 661–666, April 2005
Karaman, S., Frazzoli, E.: Sampling-based algorithms for optimal motion planning. Int. J. Robot. Res. 30(7), 846–894 (2011)
Chen, S., Li, Y., Kwok, N.M.: Active vision in robotic systems: a survey of recent developments. Int. J. Robot. Res. 30(11), 1343–1377 (2011)
Soatto, S.: Actionable information in vision. In: Cipolla, R., Battiato, S., Farinella, G.M. (eds.) Machine Learning for Computer Vision, pp. 17–48. Springer, Heidelberg (2013)
Davison, A.J., Murray, D.W.: Simultaneous localization and map-building using active vision. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 865–880 (2002)
Sadat, S.A., Chutskoff, K., Jungic, D., Wawerla, J., Vaughan, R.: Feature-rich path planning for robust navigation of MAVs with mono-SLAM. In: IEEE International Conference on Robotics and Automation (ICRA) (2014)
Achtelik, M.W., Lynen, S., Weiss, S., Chli, M., Siegwart, R.: Motion-and uncertainty-aware path planning for micro aerial vehicles. J. Field Robot. 31(4), 676–698 (2014)
Kim, A., Eustice, R.M.: Perception-driven navigation: active visual slam for robotic area coverage. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 3196–3203. IEEE (2013)
Irani, M., Anandan, P.: All about direct methods. In: Vision Algorithms: Theory and Practice, pp. 267–277. Springer (2000)
Newcombe, R.A., Lovegrove, S.J., Davison, A.J.: DTAM: dense tracking and mapping in real-time. In: IEEE International Conference on Computer Vision (ICCV), pp. 2320–2327 (2011)
Ferguson, D., Kalra, N., Stentz, A.: Replanning with rrts. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 1243–1248. IEEE (2006)
Boardman, B.L., Harden, T.A., Martinez, S.: Optimal kinodynamic motion planning in environments with unexpected obstacles. Technical report, Los Alamos National Laboratory (LANL) (2014)
Otte, M., Frazzoli, E.: RRT-X: real-time motion planning/replanning for environments with unpredictable obstacles. In: International Workshop on the Algorithmic Foundations of Robotics (WAFR) (2014)
Schmid, K., Lutz, P., Tomić, T., Mair, E., Hirschmüller, H.: Autonomous vision-based micro air vehicle for indoor and outdoor navigation. J. Field Robot. 31(4), 537–570 (2014)
Barfoot, T.D., Furgale, P.T.: Associating uncertainty with three-dimensional poses for use in estimation problems. IEEE Trans. Robot. 30, 679–693 (2014)
Meilland, M., Comport, A.I.: On unifying key-frame and voxel-based dense visual SLAM at large scales. In: IEEE International Conference on Intelligent Robots and Systems (IROS) (2013)
Haner, S., Heyden, A.: Optimal view path planning for visual slam. In: Heyden, A., Kahl, F. (eds.) Image Analysis, pp. 370–380. Springer, Heidelberg (2011)
Hornung, A., Wurm, K.M., Bennewitz, M., Stachniss, C., Burgard, W.: Octomap: an efficient probabilistic 3d mapping framework based on octrees. Auton. Robot. 34(3), 189–206 (2013)
Mason, J., Ricco, S., Parr, R.: Textured occupancy grids for monocular localization without features. In: IEEE International Conference on Robotics and Automation (ICRA), Shanghai, China (2011)
Pizzoli, M., Forster, C., Scaramuzza, D.: REMODE: probabilistic, monocular dense reconstruction in real time. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 2609–2616. IEEE, May 2014
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Costante, G., Delmerico, J., Werlberger, M., Valigi, P., Scaramuzza, D. (2018). Exploiting Photometric Information for Planning Under Uncertainty. 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_7
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DOI: https://doi.org/10.1007/978-3-319-51532-8_7
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