Name
Abstract | ||
|---|---|---|
3D Lidar has become a widely used sensor technology in autonomous vehicles by providing accurate distance information. However, lidar pointcloud processing often involves sophisticated algorithms, and takes a lot of computational power. Many prior approaches relied on a GPU-based parallel programming model, such as CUDA, to accelerate these computations. However, little attention has been given to comparing different methods for selecting the most-suited programming and parallelization approaches for a given computing system. We present our findings and insights identified by implementing various parallel approaches considering both CPUs and GPUs. We also demonstrate significant acceleration results using a realworld perception algorithm developed to detect road boundaries. Finally, we compare the pros and cons of each method in terms of system architecture, programming model, and resource utilization to yield a better understanding of choosing the best parallelization approach for a given optimization objective. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/RTCSA50079.2020.9203651 | 2020 IEEE 26th International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA) |
DocType | ISBN | Citations |
Conference | 978-1-7281-4403-0 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Iljoo Baek | 1 | 1 | 1.38 |
| Kamal Fuseini | 2 | 0 | 0.34 |
| Ragunathan Raj Rajkumar | 3 | 2 | 2.06 |