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Stereo camera visual SLAM with hierarchical masking and motion-state classification at outdoor construction sites containing large dynamic objects
Advanced Robotics ( IF 1.4 ) Pub Date : 2021-01-11
Runqiu Bao, Ren Komatsu, Renato Miyagusuku, Masaki Chino, Atsushi Yamashita, Hajime Asama

ABSTRACT

At modern construction sites, utilizing GNSS (Global Navigation Satellite System) to measure the real-time location and orientation (i.e. pose) of construction machines and navigate them is very common. However, GNSS is not always available. Replacing GNSS with on-board cameras and visual simultaneous localization and mapping (visual SLAM) to navigate the machines is a cost-effective solution. Nevertheless, at construction sites, multiple construction machines will usually work together and side-by-side, causing large dynamic occlusions in the cameras' view. Standard visual SLAM cannot handle large dynamic occlusions well. In this work, we propose a motion segmentation method to efficiently extract static parts from crowded dynamic scenes to enable robust tracking of camera ego-motion. Our method utilizes semantic information combined with object-level geometric constraints to quickly detect the static parts of the scene. Then, we perform a two-step coarse-to-fine ego-motion tracking with reference to the static parts. This leads to a novel dynamic visual SLAM formation. We test our proposals through a real implementation based on ORB-SLAM2, and datasets we collected from real construction sites. The results show that when standard visual SLAM fails, our method can still retain accurate camera ego-motion tracking in real-time. Comparing to state-of-the-art dynamic visual SLAM methods, ours shows outstanding efficiency and competitive result trajectory accuracy.



中文翻译:

立体摄像机视觉SLAM,在包含大型动态对象的室外建筑工地具有分层遮罩和运动状态分类

摘要

在现代建筑工地中,利用GNSS(全球导航卫星系统)来测量建筑机械的实时位置和方向(即姿态)并进行导航非常普遍。但是,GNSS并非始终可用。用车载摄像头和可视同时定位和地图绘制(Visual SLAM)代替GNSS来导航机器是一种经济高效的解决方案。尽管如此,在建筑工地上,通常会有多台建筑机械并排工作,从而在摄像机视野中造成较大的动态遮挡。标准视觉SLAM无法很好地处理大型动态遮挡。在这项工作中,我们提出了一种运动分割方法,可以从拥挤的动态场景中有效提取静态部分,以实现对相机自我运动的鲁棒跟踪。我们的方法利用语义信息和对象级几何约束来快速检测场景的静态部分。然后,我们参照静态部分执行两步粗略到细微的自我运动跟踪。这导致了新颖的动态视觉SLAM形成。我们通过基于ORB-SLAM2的实际实施以及从实际施工现场收集的数据集来测试我们的建议。结果表明,当标准视觉SLAM失败时,我们的方法仍可以实时保持准确的相机自我运动跟踪。与最先进的动态视觉SLAM方法相比,我们的方法具有出色的效率和具有竞争力的结果轨迹精度。我们参照静态部分执行两步粗略到精细的自我运动跟踪。这导致了新颖的动态视觉SLAM形成。我们通过基于ORB-SLAM2的实际实施以及从实际施工现场收集的数据集来测试我们的建议。结果表明,当标准视觉SLAM失败时,我们的方法仍可以实时保持准确的相机自我运动跟踪。与最先进的动态视觉SLAM方法相比,我们的方法具有出色的效率和具有竞争力的结果轨迹精度。我们参照静态部分执行两步粗略到精细的自我运动跟踪。这导致了新颖的动态视觉SLAM形成。我们通过基于ORB-SLAM2的实际实施以及从实际施工现场收集的数据集来测试我们的建议。结果表明,当标准视觉SLAM失败时,我们的方法仍可以实时保持准确的相机自我运动跟踪。与最先进的动态视觉SLAM方法相比,我们的方法具有出色的效率和具有竞争力的结果轨迹精度。我们的方法仍然可以实时保持准确的相机自我运动跟踪。与最先进的动态视觉SLAM方法相比,我们的方法具有出色的效率和具有竞争力的结果轨迹精度。我们的方法仍然可以实时保持准确的相机自我运动跟踪。与最先进的动态视觉SLAM方法相比,我们的方法具有出色的效率和具有竞争力的结果轨迹精度。

更新日期:2021-01-11
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