Towards real-time LiDAR processing on RISC-V-based ASIPs: fast trigonometric approximations via parabolic synthesis

The real-time processing of LiDAR data is a pivotal aspect of numerous autonomous driving and robotics operations. However, the sheer amount of data requiring computationally complex calculations presents a significant challenge for embedded processors operating under tight real-time constraints. So far, embedded processors were mainly used for the retrieval of LiDAR data, offloading computationally demanding tasks to a coprocessor such as a GPU or a specialized vector processor. This work focuses on processing LiDAR data directly on an embedded, low-power, 3-stage RISC-V (RV32IMFCB) core. To study LiDAR performance, the segmentation stage of LeGO-LOAM - a lightweight and ground-optimized LiDAR odometry and mapping framework - was implemented and extended as a reference algorithm. It consists of ground-detection as well as clustering based on raw data from a Velodyne LiDAR. Profiling revealed that most of the runtime is spent computing sine and arc tangent functions. A special function unit based on parabolic synthesis has been developed, capable of computing sine and arc tangent with more than 16 bit accuracy in two clock cycles. Its implementation is described and evaluated against alternative optimizations, such as a fixed-point software approximation and a CORDIC based unit for trigonometric function approximation. The approaches were executed and verified using RTL simulation. Additionally, all variants of the core have been synthesized in a 22 nm FD-SOI technology. Best performance is reached using parabolic synthesis, which improves runtime as well as power efficiency by more than 3× with a slight 5% increase in area compared to the baseline model. Finally, the results demonstrate that the real-time goal of 20 FPS for autonomous driving applications is achievable, enabling customized, low-power RISC-V cores to be applied in LiDAR processing pipelines.