Higashi Daijiro, Fukuta Naoki, Tasaki Tsuyoshi
Graduate School of Science and Technology, Meijo University, Nagoya, Japan.
Front Robot AI. 2025 Jun 12;12:1560342. doi: 10.3389/frobt.2025.1560342. eCollection 2025.
Obstacle avoidance is important for autonomous driving. Metric scale obstacle detection using a monocular camera for obstacle avoidance has been studied. In this study, metric scale obstacle detection means detecting obstacles and measuring the distance to them with a metric scale. We have already developed PMOD-Net, which realizes metric scale obstacle detection by using a monocular camera and a 3D map for autonomous driving. However, PMOD-Net's distance error of non-fixed obstacles that do not exist on the 3D map is large. Accordingly, this study deals with the problem of improving distance estimation of non-fixed obstacles for obstacle avoidance. To solve the problem, we focused on the fact that PMOD-Net simultaneously performed object detection and distance estimation. We have developed a new loss function called "DifSeg." DifSeg is calculated from the distance estimation results on the non-fixed obstacle region, which is defined based on the object detection results. Therefore, DifSeg makes PMOD-Net focus on non-fixed obstacles during training. We evaluated the effect of DifSeg by using CARLA simulator, KITTI, and an original indoor dataset. The evaluation results showed that the distance estimation accuracy was improved on all datasets. Especially in the case of KITTI, the distance estimation error of our method was 2.42 m, which was 2.14 m less than that of the latest monocular depth estimation method.
避障对于自动驾驶至关重要。人们已经研究了使用单目相机进行避障的公制尺度障碍物检测。在本研究中，公制尺度障碍物检测是指检测障碍物并以公制尺度测量到它们的距离。我们已经开发了PMOD-Net，它通过使用单目相机和3D地图来实现自动驾驶中的公制尺度障碍物检测。然而，PMOD-Net对于3D地图上不存在的非固定障碍物的距离误差较大。因此，本研究致力于解决提高用于避障的非固定障碍物距离估计的问题。为了解决这个问题，我们关注到PMOD-Net同时进行目标检测和距离估计这一事实。我们开发了一种名为“DifSeg”的新损失函数。DifSeg是根据基于目标检测结果定义的非固定障碍物区域上的距离估计结果计算得出的。因此，DifSeg使PMOD-Net在训练期间专注于非固定障碍物。我们使用CARLA模拟器、KITTI和一个原始室内数据集评估了DifSeg的效果。评估结果表明，在所有数据集上距离估计精度都得到了提高。特别是在KITTI的情况下，我们方法的距离估计误差为2.42米，比最新的单目深度估计方法少2.14米。
