Abstract
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.
Abstract (translated)
我们提出了一种实时、无需学习的深度估计方法，该方法融合了激光雷达（LiDAR）数据与立体相机输入。我们的方法包含三种关键技术：带有离散视差匹配代价（DDC）的半全局匹配（SGM）立体视觉、激光雷达视差半密集化以及结合立体图像和激光雷达数据的一致性检查。每个组件都设计为可以在GPU上并行化以实现实时性能。在KITTI数据集上的评估显示，所提出的方法达到了2.79%的误差率，优于之前的最先进的实时立体-LiDAR融合方法，其误差率为3.05%。此外，我们还在各种场景中测试了该方法，包括不同激光雷达点密度、变化天气条件和室内环境，以展示其高度适应性。我们认为，我们的方法具有实时性和无需学习的特性，使其在机器人技术和自动化应用中非常实用。
URL
https://arxiv.org/abs/2504.05148
