不依赖学习的实时立体激光融合方法，精度优于现有方案。
Stereo-LiDAR Fusion by Semi-Global Matching With Discrete Disparity-Matching Cost and Semidensification
- 用离散视差匹配代价改进SGM，提升立体匹配精度
- 在KITTI上误差率2.79%，低于此前最优方法的3.05%
- 无需训练，适合机器人与自动化场景部署
我们提出一种实时、非学习的深度估计方法，融合激光雷达（LiDAR）与双目相机数据。该方法包含三个关键技术：基于离散视差匹配代价（DDC）的半全局匹配（SGM）立体匹配、激光雷达视差的半稠密化处理，以及结合立体图像与激光雷达数据的一致性验证。各组件均设计为可在GPU上并行执行，实现实时性能。在KITTI数据集上的评估显示，该方法误差率为2.79%，优于此前最先进的实时立体-激光融合方法（3.05%）。此外，我们在不同激光雷达点密度、多种天气条件及室内环境进行了测试，验证了方法的高适应性。我们认为，该方法的实时性与非学习特性使其在机器人与自动化应用中具有高度实用性。
原文摘要 · Abstract (English)
We present a real-time, non-learning depth estimation method that fuses Light Detection and Ranging (LiDAR) data with stereo camera input. Our approach comprises three key techniques: Semi-Global Matching (SGM) stereo with Discrete Disparity-matching Cost (DDC), semidensification of LiDAR disparity, and a consistency check that combines stereo images and LiDAR data. Each of these components is designed for parallelization on a GPU to realize real-time performance. When it was evaluated on the KITTI dataset, the proposed method achieved an error rate of 2.79\%, outperforming the previous state-of-the-art real-time stereo-LiDAR fusion method, which had an error rate of 3.05\%. Furthermore, we tested the proposed method in various scenarios, including different LiDAR point densities, varying weather conditions, and indoor environments, to demonstrate its high adaptability. We believe that the real-time and non-learning nature of our method makes it highly practical for applications in robotics and automation.
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