Landing sites detection using LiDAR data on manycore systems
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 in parallel on multi- and...
| Authors: | , , , , , |
|---|---|
| Format: | article |
| Publication Date: | 2017 |
| Country: | España |
| Institution: | Universidad de Santiago de Compostela (USC) |
| Repository: | Minerva. Repositorio Institucional de la Universidad de Santiago de Compostela |
| Language: | English |
| OAI Identifier: | oai:minerva.usc.gal:10347/38912 |
| Online Access: | https://hdl.handle.net/10347/38912 |
| Access Level: | Open access |
| Keyword: | LiDAR Landing zone detection Load balancing Xeon Phi |
| Summary: | 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 in parallel on multi- and 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 in this work. 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. |
|---|