Developing cost-efficient and reliable perception systems remains a central challenge for automated vehicles. LiDAR and camera-based systems dominate, yet they present trade-offs in cost, robustness and performance under adverse conditions. This work introduces a novel framework for learning-based 3D semantic segmentation using Calyo Pulse, a modular, solid-state 3D ultrasound sensor system for use in harsh and cluttered environments. A 3D U-Net architecture is introduced and trained on the spatial ultrasound data for volumetric segmentation. Results demonstrate robust segmentation performance from Calyo Pulse sensors, with potential for further improvement through larger datasets, refined ground truth, and weighted loss functions. Importantly, this study highlights 3D ultrasound sensing as a promising complementary modality for reliable autonomy.

翻译：开发成本效益高且可靠的感知系统仍然是自动驾驶车辆面临的核心挑战。尽管激光雷达和基于摄像头的系统占据主导地位，但它们在恶劣条件下的成本、鲁棒性和性能方面存在权衡。本研究提出了一种新颖的基于学习的3D语义分割框架，该框架采用Calyo Pulse——一种模块化、固态的3D超声传感器系统，适用于恶劣和杂乱的环境。我们引入了一种3D U-Net架构，并在空间超声数据上进行了训练，以实现体素级分割。结果表明，Calyo Pulse传感器能够实现稳健的分割性能，并且通过使用更大的数据集、更精细的真值标注以及加权损失函数，其性能还有进一步提升的潜力。重要的是，本研究强调了3D超声传感作为一种有前景的补充模态，为实现可靠的自动驾驶提供了可能。