Helicopters are widely used in emergency situations, where knowing if a geographical location is adequate for landing is a critical issue, and it is far from being a straightforward task.
In this work, we present a method to detect and classify landing sites from LiDAR data.
Landing sites are detected in parallel on manycore systems using OpenMP.
Load balancing was identified as the main cause of poor performance because the computational cost depends mainly on the input data.
Results for a set of LiDAR point clouds that represent different real scenarios were used as case studies. Balancing strategies for three different multi- and manycore systems were analyzed.
The proposed load balancing techniques increase performance up to three times from the unbalanced case.
Keywords: LiDAR, landing zone detection, load balancing, Xeon Phi