Obstacle detection is a safety-critical problem in robot navigation, where stereo matching is a popular vision-based approach. While deep neural networks have shown impressive results in computer vision, most of the previous obstacle detection works only leverage traditional stereo matching techniques to meet the computational constraints for real-time feedback. This paper proposes a computationally efficient method that employs a deep neural network to detect occupancy from stereo images directly. Instead of learning the point cloud correspondence from the stereo data, our approach extracts the compact obstacle distribution based on volumetric representations. In addition, we prune the computation of safety irrelevant spaces in a coarse-to-fine manner based on octrees generated by the decoder. As a result, we achieve real-time performance on the onboard computer (NVIDIA Jetson TX2). Our approach detects obstacles accurately in the range of 32 meters and achieves better IoU (Intersection over Union) and CD (Chamfer Distance) scores with only 2% of the computation cost of the state-of-the-art stereo model. Furthermore, we validate our method's robustness and real-world feasibility through autonomous navigation experiments with a real robot. Hence, our work contributes toward closing the gap between the stereo-based system in robot perception and state-of-the-art stereo models in computer vision. To counter the scarcity of high-quality real-world indoor stereo datasets, we collect a 1.36 hours stereo dataset with a mobile robot which is used to fine-tune our model. The dataset, the code, and further details including additional visualizations are available at https://lhy.xyz/stereovoxelnet

翻译：障碍物检测是机器人导航中关乎安全的关键问题，其中立体匹配是一种流行的视觉方法。尽管深度神经网络在计算机视觉领域取得了显著成果，但以往大多数障碍物检测工作仅利用传统立体匹配技术以满足实时反馈的计算约束。本文提出一种计算高效的方法，采用深度神经网络直接从双目图像检测占用状态。我们的方法并非从立体数据学习点云对应关系，而是基于体素表征提取紧凑的障碍物分布。此外，我们利用解码器生成的八叉树，以由粗到精的方式裁剪与安全无关的空间计算量。最终，我们在车载计算机（NVIDIA Jetson TX2）上实现了实时性能。该方法能在32米范围内精确检测障碍物，在仅消耗最先进立体模型2%计算成本的情况下，取得了更优的交并比（IoU）和倒角距离（CD）得分。此外，我们通过真实机器人自主导航实验验证了方法的鲁棒性和实际可行性。因此，本工作有助于缩小机器人感知中的立体视觉系统与计算机视觉领域最先进立体模型之间的差距。针对高质量真实室内立体数据集匮乏的问题，我们利用移动机器人采集了1.36小时的立体数据集，用于微调模型。数据集、代码及包含额外可视化在内的更多详情，请访问 https://lhy.xyz/stereovoxelnet