Abstract
Accurate position estimation is essential for modern navigation systems deployed in autonomous platforms, including ground vehicles, marine vessels, and aerial drones. In this context, Visual Simultaneous Localisation and Mapping (VSLAM) - which includes Visual Odometry - relies heavily on the reliable extraction of salient feature points from the visual input data. In this work, we propose an embedded implementation of an unsupervised architecture capable of detecting and describing feature points. It is based on a quantised SuperPoint convolutional neural network. Our objective is to minimise the computational demands of the model while preserving high detection quality, thus facilitating efficient deployment on platforms with limited resources, such as mobile or embedded systems. We implemented the solution on an FPGA System-on-Chip (SoC) platform, specifically the AMD/Xilinx Zynq UltraScale+, where we evaluated the performance of Deep Learning Processing Units (DPUs) and we also used the Brevitas library and the FINN framework to perform model quantisation and hardware-aware optimisation. This allowed us to process 640 x 480 pixel images at up to 54 fps on an FPGA platform, outperforming state-of-the-art solutions in the field. We conducted experiments on the TUM dataset to demonstrate and discuss the impact of different quantisation techniques on the accuracy and performance of the model in a visual odometry task.
Abstract (translated)
准确的位置估计对于部署在自主平台（包括地面车辆、海洋船只和无人机）上的现代导航系统至关重要。在此背景下，视觉同时定位与地图构建 (VSLAM) 依赖于从视觉输入数据中可靠地提取显著特征点，其中包含视觉里程计。本文提出了一种嵌入式实现方案，该方案基于未监督架构，并能够检测和描述特征点。我们的方法采用量化后的 SuperPoint 卷积神经网络。 我们的目标是通过保持高检测质量的同时最小化模型的计算需求，从而在资源有限（如移动或嵌入式系统）平台上实现高效部署。我们在 AMD/Xilinx Zynq UltraScale+ FPGA 系统级芯片 (SoC) 平台上实现了这一解决方案，并评估了深度学习处理单元(DPUs) 的性能。此外，我们使用 Brevitas 库和 FINN 框架进行模型量化及硬件感知优化。这使我们能够在FPGA平台上以高达54 fps的帧率处理640x480像素图像，优于该领域的现有解决方案。 我们在 TUM 数据集上进行了实验，展示了不同量化技术对视觉里程计任务中模型精度和性能的影响，并讨论了这些影响。
URL
https://arxiv.org/abs/2507.07903
