This paper focuses on the motion estimation problem of ground vehicles using odometry and monocular visual sensors. While the keyframe-based batch optimization methods become the mainstream approach in mobile vehicle localization and mapping, the keyframe poses are usually represented by SE(3) in vision-based methods or SE(2) in methods based on range scanners. For a ground vehicle, this paper proposes a new SE(2)-constrained SE(3) parameterization of its poses, which can be easily achieved in the batch optimization framework using specially formulated edges. Utilizing such a parameterization of poses, a complete odometry-vision-based motion estimation system is developed. The system is designed in a commonly used structure of graph optimization, providing high modularity and flexibility for further implementation or adaptation. Its superior performance in terms of accuracy on a ground vehicle platform is validated by real-world experiments in industrial indoor environments.

中文翻译：

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具有 SE(2) 约束 SE(3) 位姿的基于里程计视觉的地面车辆运动估计


本文重点研究使用里程计和单目视觉传感器的地面车辆的运动估计问题。虽然基于关键帧的批量优化方法成为移动车辆定位和建图的主流方法，但关键帧位姿通常在基于视觉的方法中用SE(3)表示，在基于距离扫描仪的方法中通常用SE(2)表示。对于地面车辆，本文提出了一种新的 SE(2) 约束 SE(3) 姿态参数化，可以使用特殊制定的边缘在批量优化框架中轻松实现。利用这种姿态参数化，开发了一个完整的基于里程计视觉的运动估计系统。该系统采用常用的图优化结构进行设计，为进一步的实现或适配提供了高度的模块化和灵活性。其在地面车辆平台上的准确性方面的卓越性能已通过工业室内环境中的实际实验得到验证。