This paper proposes the object depth estimation in real-time, using only a monocular camera in an onboard computer with a low-cost GPU. Our algorithm estimates scene depth from a sparse feature-based visual odometry algorithm and detects/tracks objects’ bounding box by utilizing the existing object detection algorithm in parallel. Both algorithms share their results, i.e., feature, motion, and bounding boxes, to handle static and dynamic objects in the scene. We validate the scene depth accuracy of sparse features with KITTI and its ground-truth depth map made from LiDAR observations quantitatively, and the depth of detected object with the Hyundai driving datasets and satellite maps qualitatively. We compare the depth map of our algorithm with the result of (un-) supervised monocular depth estimation algorithms. The validation shows that our performance is comparable to that of monocular depth estimation algorithms which train depth indirectly (or directly) from stereo image pairs (or depth image), and better than that of algorithms trained with monocular images only, in terms of the error and the accuracy. Also, we confirm that our computational load is much lighter than the learning-based methods, while showing comparable performance.

本文提出了实时对象深度估计，仅在具有低成本 GPU 的机载计算机中使用单目相机。我们的算法通过基于稀疏特征的视觉里程计算法估计场景深度，并通过并行利用现有的对象检测算法来检测/跟踪对象的边界框。两种算法共享其结果，即特征、运动和边界框，以处理场景中的静态和动态对象。我们使用 KITTI 及其基于 LiDAR 观测的地面实况深度图定量验证稀疏特征的场景深度精度，并使用现代驾驶数据集和卫星图定性验证检测到的物体的深度。我们将我们算法的深度图与（非）监督单目深度估计算法的结果进行比较。验证表明，我们的性能与从立体图像对（或深度图像）间接（或直接）训练深度的单眼深度估计算法相当，并且在误差方面优于仅使用单眼图像训练的算法和准确性。此外，我们确认我们的计算负载比基于学习的方法轻得多，同时表现出可比的性能。