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3D normal distributions transform occupancy maps: An efficient representation for mapping in dynamic environments.

, , , and . Int. J. Robotics Res., 32 (14): 1627-1644 (2013)

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3D normal distributions transform occupancy maps: An efficient representation for mapping in dynamic environments., , , and . Int. J. Robotics Res., 32 (14): 1627-1644 (2013)Building an Enhanced Vocabulary of the Robot Environment with a Ceiling Pointing Camera., , , , and . Sensors, 16 (4): 493 (2016)PointNet and geometric reasoning for detection of grape vines from single frame RGB-D data in outdoor conditions., , , and . NLDL, page 1-6. Septentrio Academic Publishing, (2020)Normal Distributions Transform Occupancy Maps: Application to large-scale online 3D mapping., , , , and . ICRA, page 2233-2238. IEEE, (2013)Independent Markov chain occupancy grid maps for representation of dynamic environment., , and . IROS, page 3489-3495. IEEE, (2012)Comparative Evaluation of Range Sensor Accuracy in Indoor Environments., , , and . ECMR, page 19-24. Learning Systems Lab, AASS, Örebro University, (2011)Robotic Platform for Precise Mechanical Stress Induction in Greenhouses Cultivation., , and . CASE, page 1558-1565. IEEE, (2020)MALTA: A System of Multiple Autonomous Trucks for Load Transportation., , , and . ECMR, page 93-98. KoREMA, (2009)Estimating the 3D Position of Humans Wearing a Reflective Vest Using a Single Camera System., and . FSR, volume 92 of Springer Tracts in Advanced Robotics, page 143-157. Springer, (2012)SLAM auto-complete: Completing a robot map using an emergency map., , , and . SSRR, page 35-40. IEEE, (2017)