Predicting Unobserved Space for Planning via Depth Map Augmentation

@article{Fehr2019PredictingUS,
  title={Predicting Unobserved Space for Planning via Depth Map Augmentation},
  author={Marius Fehr and Tim Taubner and Yang Liu and Roland Y. Siegwart and C{\'e}sar Cadena},
  journal={2019 19th International Conference on Advanced Robotics (ICAR)},
  year={2019},
  pages={30-36}
}
Safe and efficient path planning is crucial for autonomous mobile robots. A prerequisite for path planning is to have a comprehensive understanding of the 3D structure of the robot's environment. On Micro Air Vehicles (MAVs) this is commonly achieved using low-cost sensors, such as stereo or RGB-D cameras. These sensors may fail to provide depth measurements in textureless or IR-absorbing areas and have limited effective range. In path planning, this results in inefficient trajectories or… 
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