Region of Interest Segmentation from Lidar Point Cloud for Multirotor Aerial Vehicles.

We propose a novel filter for segmenting the region 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, a point cloud is transformed into a depth image and then places with a high density near to the vehicle (local maxima) are identified. Then we merge 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 a range angle image is constructed, smoothing is applied to reduce the noise. Finally, we find out connected components in the improved depth image while incorporating smoothed range angle image. This allows separating the region of interest. The filter has been tested on a simulated environment as well as an actual drone and provides real-time performance. We make our source code and dataset available online\footnote[2]{Source code and dataset are available at \url{https://github.com/GPrathap/hagen.git}}. Real world experiment result can be found \footnote[3]{Real-world experiment result can be found on the following link: \url{https://www.youtube.com/watch?v=iHd_ZkhKPjc}}