Abstract
There are a range of small-size robots that cannot afford to mount a three-dimensional sensor due to energy, size or power limitations. However, the best localization and mapping algorithms and object recognition methods rely on a three-dimensional representation of the environment to provide enhanced capabilities. Thus, in this work we propose a method to create a dense three-dimensional representation of the environment by fusing the output of a KSLAM algorithm with predicted point clouds. We demonstrate with quantitative and qualitative results the advantages of our method, focusing in three different measures: localization accuracy, densification capabilities and accuracy of the resultant three-dimensional map.
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Acknowledgements
This work has been supported by the Spanish Government TIN2016-76515R Grant, supported with Feder funds and by a Spanish Government grant for cooperating in research tasks ID 998142. This work has also been supported by a Spanish grant for PhD studies ACIF/2017/243 and FPU16/00887. Thanks to Nvidia for the generous donation of a Titan Xp and a Quadro P6000.
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Torres-Camara, J.M., Escalona, F., Gomez-Donoso, F., Cazorla, M. (2020). Map Slammer: Densifying Scattered KSLAM 3D Maps with Estimated Depth. In: Silva, M., LuÃs Lima, J., Reis, L., Sanfeliu, A., Tardioli, D. (eds) Robot 2019: Fourth Iberian Robotics Conference. ROBOT 2019. Advances in Intelligent Systems and Computing, vol 1093. Springer, Cham. https://doi.org/10.1007/978-3-030-36150-1_46
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