Abstract
As unmanned aerial vehicles (UAVs) are used in challenging environments to carry out various complex tasks, a satisfactory level of localization performance is required to ensure safe and reliable operations. 3D laser range finder (LRF)-based localization is a suitable approach in areas where GPS signal is not accesible or unreliable. During navigation, environmental information and map noises at different locations may contribute differently to a UAV’s localization process, causing it to have dissimilar ability to localize itself using LRF readings, which is referred to as localizability in this paper. We propose a localizability constraint (LC) based path planning method for UAV, which plans the navigation path according to LRF sensor model to achieve higher localization performance throughout the path. Paths planned with and without LC are compared and discussed through simulations in outdoor urban and wilderness environemnts. We show that the proposed method effectively reduces the localization error along the planned paths.
This work is supported by the National Key Research and Development Program of China (Grant No. 2017YFB1302200), and supported in part by the Natural Science Foundation of China (Grant No. 61573243 and 61773261).
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Irani, B., Chen, W., Wang, J. (2019). A Localizability Constraint-Based Path Planning Method for Unmanned Aerial Vehicle. In: Strand, M., Dillmann, R., Menegatti, E., Ghidoni, S. (eds) Intelligent Autonomous Systems 15. IAS 2018. Advances in Intelligent Systems and Computing, vol 867. Springer, Cham. https://doi.org/10.1007/978-3-030-01370-7_71
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