| Reference : Robust Online Obstacle Detection and Tracking for Collision-free Navigation of Multir... |
| Scientific congresses, symposiums and conference proceedings : Paper published in a book | |||
| Engineering, computing & technology : Aerospace & aeronautics engineering Engineering, computing & technology : Computer science Engineering, computing & technology : Multidisciplinary, general & others | |||
| http://hdl.handle.net/10993/38138 | |||
| Robust Online Obstacle Detection and Tracking for Collision-free Navigation of Multirotor UAVs in Complex Environments | |
| English | |
Wang, Min  [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >] | |
| Voos, Holger [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit > ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)] | |
| Su, Daobilige [University of Sydney > Australian Center for Field Robotics (ACFR)] | |
| 2018 | |
| 15th International Conference on Control, Automation, Robotics and Vision (ICARCV), Singapore 18-21 November 2018 | |
| 1228 - 1234 | |
| Yes | |
| International | |
| The 15th International Conference on Control, Automation, Robotics and Vision | |
| from 18-11-2018 to 21-11-2018 | |
| Singapore | |
| Singapore | |
| [en] Sensing ; UAV ; Collision-free Navigation | |
| [en] Object detection and tracking is a challenging task, especially for unmanned aerial robots in complex environments where both static and dynamic objects are present. It is, however, essential for ensuring safety of the robot during navigation in such environments. In this work we present a practical online approach which is based on a 2D LIDAR. Unlike common approaches in the literature of modeling the environment as 2D or 3D occupancy grids, our approach offers a fast and robust method to represent the objects in the environment in a compact form, which is significantly more efficient in terms of both memory and computation in comparison with the former. Our approach is also capable of classifying objects into categories such as static and dynamic, and tracking dynamic objects as well as estimating their velocities with reasonable accuracy. | |
| Researchers ; Professionals ; Students ; General public ; Others | |
| http://hdl.handle.net/10993/38138 | |
| FnR ; FNR10484117 > Holger Voos > BEST-RPAS > Robust Emergency Sense-and-Avoid Capability for Small Remotely Piloted Aerial Systems > 01/02/2016 > 31/01/2019 > 2015 |
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