用神经网络直接从全景图像估计距离，不依赖精确标定。
Learning-Based Distance Estimation for 360° Single-Sensor Setups
- 用单个全景镜头的原始图像训练神经网络，直接预测距离。
- 在三个数据集上表现优于传统几何方法和其它学习基线。
- 适合低成本机器人、自动驾驶和监控等实时场景应用。
准确的距离估计是机器人感知中的基础挑战，尤其在全向成像中，传统几何方法因镜头畸变和环境变化而表现不佳。本文提出一种基于神经网络的单目距离估计方法，仅使用一个360°鱼眼镜头相机。与依赖精确镜头标定的经典三角测量技术不同，该方法直接从原始全向输入中学习并推断物体距离，具有更强的鲁棒性和适应性。我们在三个360°数据集（LOAF、ULM360和新采集的Boat360）上评估该方法，每个数据集代表不同的环境和传感器配置。实验结果表明，所提出的基于学习的模型在准确性和鲁棒性方面均优于传统几何方法及其他学习基线。这些发现展示了深度学习在实时全向距离估计中的潜力，使本方法特别适用于低成本机器人、自主导航和监控应用。
原文摘要 · Abstract (English)
Accurate distance estimation is a fundamental challenge in robotic perception, particularly in omnidirectional imaging, where traditional geometric methods struggle with lens distortions and environmental variability. In this work, we propose a neural network-based approach for monocular distance estimation using a single 360° fisheye lens camera. Unlike classical trigonometric techniques that rely on precise lens calibration, our method directly learns and infers the distance of objects from raw omnidirectional inputs, offering greater robustness and adaptability across diverse conditions. We evaluate our approach on three 360° datasets (LOAF, ULM360, and a newly captured dataset Boat360), each representing distinct environmental and sensor setups. Our experimental results demonstrate that the proposed learning-based model outperforms traditional geometry-based methods and other learning baselines in both accuracy and robustness. These findings highlight the potential of deep learning for real-time omnidirectional distance estimation, making our approach particularly well-suited for low-cost applications in robotics, autonomous navigation, and surveillance.
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