Zheng Fan, Tang Hengbo, Liu Yun-Hui
IEEE Trans Cybern. 2019 Jul;49(7):2652-2663. doi: 10.1109/TCYB.2018.2831900. Epub 2018 May 10.
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.
本文聚焦于使用里程计和单目视觉传感器的地面车辆运动估计问题。虽然基于关键帧的批量优化方法已成为移动车辆定位与地图构建的主流方法，但在基于视觉的方法中，关键帧位姿通常由SE(3)表示，而在基于距离扫描仪的方法中则由SE(2)表示。针对地面车辆，本文提出了一种新的位姿SE(2)约束的SE(3)参数化方法，该方法可通过使用特殊构建的边在批量优化框架中轻松实现。利用这种位姿参数化方法，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设计，为进一步实现或适配提供了高模块化和灵活性。通过在工业室内环境中的实际实验验证了其在地面车辆平台上的精度方面的卓越性能。
