Edge Computing for Mobile Robots: Multi-Robot Feature-Based Lidar Odometry with FPGAs - L. Qingqing; F. Yuhong; Jorge Peña Queralta; T. N. Gia; H. Tenhunen; Z. Zou; T. Westerlund (United States, 2022)
Offloading computationally intensive tasks such as lidar or visual odometry from mobile robots has multiple benefits. Resource constrained robots can make use of their network capabilities to reduce the data processing load and be able to perform a larger number tasks in a more efficient manner. However, previous works have mostly focused on cloud offloading, which increases latency and reduces reliability, or high-end edge devices. Instead, we explore the utilization of FPGAs at the edge for computational offloading with minimal latency and high parallelism. We present the potential for modelling feature-based odometry in VHDL and utilizing FPGA implementations.
2019 Twelfth International Conference on Mobile Computing and Ubiquitous Network (ICMU)