  We propose a novel filter for segmenting the regions of interest from LiDAR
3D point cloud for multirotor aerial vehicles. It is specially targeted for
real-time applications and works on sparse LiDAR point clouds without
preliminary mapping. We use this filter as a crucial component of fast obstacle
avoidance system for agriculture drone operating at low altitude. As the first
step, each point cloud is transformed into a depth image and then identify
places near to the vehicle (local maxima) by locating areas with high pixel
densities. Afterwards, we merge the original depth image with identified
locations after maximizing intensities of pixels in which local maxima were
obtained. Next step is to calculate the range angle image that represents
angles between two consecutive laser beams based on the improved depth image.
Once the corresponding range angle image is constructed, smoothing is applied
to reduce the noise. Finally, we find out connected components within the
improved depth image while incorporating smoothed range angle image. This
allows separating the regions of interest. The filter has been tested on
various simulated environments as well as an actual drone and provides
real-time performance. We make our source code, dataset available at
https://github.com/GPrathap/hagen.git and real world experiment result can be
found on the following link: https://www.youtube.com/watch?v=iHd_ZkhKPjc
available online.
