论文标题
Birdslam：单眼的多体大满贯在鸟眼中
BirdSLAM: Monocular Multibody SLAM in Bird's-Eye View
论文作者
论文摘要
在本文中，我们提出了Birdslam，这是一种新颖的同时本地化和映射（SLAM）系统，用于仅配备单眼相机的自主驾驶平台的挑战性情况。 Birdslam通过使用拼字法（Bird's Eye）视图作为局部化和映射的配置空间来应对其他单眼大满贯系统面临的挑战（例如单眼重建，动态对象定位和功能表示形式的不确定性）所面临的挑战。通过仅假设自我相机的高度在地面上方，Birdslam利用单视图提示将自我车辆和所有其他交通参与者准确地定位在Bird's-eye视图中。我们证明，我们的系统优于严格使用更多信息的先前工作，并通过消融分析强调了每个设计决策的相关性。
In this paper, we present BirdSLAM, a novel simultaneous localization and mapping (SLAM) system for the challenging scenario of autonomous driving platforms equipped with only a monocular camera. BirdSLAM tackles challenges faced by other monocular SLAM systems (such as scale ambiguity in monocular reconstruction, dynamic object localization, and uncertainty in feature representation) by using an orthographic (bird's-eye) view as the configuration space in which localization and mapping are performed. By assuming only the height of the ego-camera above the ground, BirdSLAM leverages single-view metrology cues to accurately localize the ego-vehicle and all other traffic participants in bird's-eye view. We demonstrate that our system outperforms prior work that uses strictly greater information, and highlight the relevance of each design decision via an ablation analysis.
