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：虽然深神经网络在计算机视觉方面显示了令人印象深刻的结果,但先前的大多数障碍检测工作只能利用传统的立体匹配技术来满足实时反馈的计算限制。本文建议采用一种计算高效的方法,利用深神经网络直接从立体图像中检测占用情况。我们的方法不是从立体数据中学习点云通信,而是根据体积表示法,提取了基于立体显示法的紧固障碍分布。此外,我们还以离心机生成的奥氏树枝粗略到裂痕的方式计算与安全无关的空间。结果,我们只能利用传统的立体匹配技术,满足实时反馈的计算限制。因此,我们在计算机上实现了实时性功能匹配技术。我们的方法在32米范围内准确检测了障碍,并实现了更好的音频联盟(Intercrection)和CD(Chamfer Learth)的分数,其计算成本只有2%。此外,我们验证了我们的方法的稳健性和现实世界可行性,通过自主导航,包括机器人的高级数据采集,我们用了一个真实的立体智能智能数据模型,我们用了一个在真实的立体智能的立体智能智能智能数据库数据库数据库中,我们用了一个更接近的系统向真实的立体智能智能数据采集的系统向真实数据。</s>