FPGA-based Acceleration of Lidar Point Cloud Processing and Detection on the Edge
- Resource Type
- Conference
- Authors
- Latotzke, Cecilia; Kloeker, Amarin; Schoening, Simon; Kemper, Fabian; Slimi, Mazen; Eckstein, Lutz; Gemmeke, Tobias
- Source
- 2023 IEEE Intelligent Vehicles Symposium (IV) Intelligent Vehicles Symposium (IV), 2023 IEEE. :1-8 Jun, 2023
- Subject
- Communication, Networking and Broadcast Technologies
Computing and Processing
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Point cloud compression
Laser radar
Power demand
Image edge detection
Object detection
Real-time systems
Sustainable development
FPGA
Lidar
ITS-S
Point Cloud Processing
Object Detection
- Language
- ISSN
- 2642-7214
Edge nodes such as Intelligent Transportation System Stations are becoming increasingly relevant in the context of automated driving as they provide connected vehicles with additional information to support their automated driving functions. However, the power budget for these edge nodes is limited and data has to be processed in real-time to be of use to automated driving functions. In this work, we present a system for processing raw lidar data in real-time on an FPGA, resulting in a significant reduction in power consumption compared to conventional hardware. Our approach leads to a 42.4% reduction in power consumption while maintaining the quality of the results. Processing two 128-layer surround-view lidar point clouds takes 522 ms per frame and an average power consumption of 39.3 W for the CPU and 34.5W for the FPGA. Our optimizations surpass the state-of-the-art by up to 193 times.