FPGA Implementation of Sketched LiDAR for a 192 x 128 SPAD Image Sensor

TL;DR

This paper introduces an FPGA-based implementation of a statistical compression algorithm for high-resolution SPAD LiDAR data, achieving significant data reduction and enabling real-time depth reconstruction.

Contribution

It presents a novel FPGA implementation of a spline-based compression algorithm optimized for SPAD LiDAR data, reducing data transfer bottlenecks in high-resolution sensors.

Findings

512x compression ratio achieved

Real-time, histogram-free depth reconstruction demonstrated

Scalable solution for future high-pixel-count SPAD arrays

Abstract

This study presents an efficient field-programmable gate array (FPGA) implementation of a polynomial spline function-based statistical compression algorithm designed to address the critical challenge of massive data transfer bandwidth in emerging high-spatial-resolution single-photon avalanche diode (SPAD) arrays, where data rates can reach tens of gigabytes per second. In our experiments, the proposed hardware implementation achieves a compression ratio of 512x compared with conventional histogram-based outputs, with the potential for further improvement. The algorithm is first optimized in software using fixed-point (FXP) arithmetic and look-up tables (LUTs) to eliminate explicit additions, multiplications, and non-linear operations. This enables a careful balance between accuracy and hardware resource utilization. Guided by this trade-off analysis, online sketch processing elements (SPEs) are implemented on an FPGA to directly process time-stamp streams from the SPAD sensor. The implementation is validated using a customized LiDAR setup with a 192 x 128-pixel SPAD array. This work demonstrates histogram-free online depth reconstruction with high fidelity, effectively alleviating the time-stamp transfer bottleneck of SPAD arrays and offering scalability as pixel counts continue to increase for future SPADs.