利用三维地图和单目摄像机估算公制比例尺非固定障碍物的距离。

IF 3 Q2 ROBOTICS
Frontiers in Robotics and AI Pub Date : 2025-06-12 eCollection Date: 2025-01-01 DOI:10.3389/frobt.2025.1560342
Daijiro Higashi, Naoki Fukuta, Tsuyoshi Tasaki
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引用次数: 0

摘要

避障对自动驾驶很重要。研究了利用单目摄像机进行公制尺度障碍物检测的避障方法。在本研究中,公制尺度障碍物检测是指用公制尺度检测障碍物并测量到障碍物的距离。我们已经开发了PMOD-Net,它通过使用单目摄像头和3D地图实现自动驾驶的公制尺度障碍物检测。然而,对于三维地图上不存在的非固定障碍物,PMOD-Net的距离误差较大。因此,本文研究了改进非固定障碍物距离估计的避障问题。为了解决这个问题,我们重点研究了PMOD-Net同时进行目标检测和距离估计的事实。我们开发了一个新的损失函数,叫做“DifSeg”。DifSeg是根据目标检测结果定义的非固定障碍物区域的距离估计结果计算得到的。因此,DifSeg使PMOD-Net在训练过程中重点关注非固定障碍。我们通过使用CARLA模拟器、KITTI和原始室内数据集来评估DifSeg的效果。评估结果表明,该方法在所有数据集上的距离估计精度都有提高。特别是在KITTI的情况下,我们的方法的距离估计误差为2.42 m,比最新的单目深度估计方法的误差小2.14 m。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Metric scale non-fixed obstacles distance estimation using a 3D map and a monocular camera.

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.

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来源期刊
CiteScore
6.50
自引率
5.90%
发文量
355
审稿时长
14 weeks
期刊介绍: Frontiers in Robotics and AI publishes rigorously peer-reviewed research covering all theory and applications of robotics, technology, and artificial intelligence, from biomedical to space robotics.
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