Mechanical Engineering School, Jiangsu University, Zhenjiang 212013, China.
Nanjing Research Institute for Agricultural Mechanization, Ministry of Agriculture, Nanjing 210014, China.
Sensors (Basel). 2022 Aug 25;22(17):6394. doi: 10.3390/s22176394.
Ego-motion estimation is a foundational capability for autonomous combine harvesters, supporting high-level functions such as navigation and harvesting. This paper presents a novel approach for estimating the motion of a combine harvester from a sequence of stereo images. The proposed method starts with tracking a set of 3D landmarks which are triangulated from stereo-matched features. Six Degree of Freedom (DoF) ego motion is obtained by minimizing the reprojection error of those landmarks on the current frame. Then, local bundle adjustment is performed to refine structure (i.e., landmark positions) and motion (i.e., keyframe poses) jointly in a sliding window. Both processes are encapsulated into a two-threaded architecture to achieve real-time performance. Our method utilizes a stereo camera, which enables estimation at true scale and easy startup of the system. Quantitative tests were performed on real agricultural scene data, comprising several different working paths, in terms of estimating accuracy and real-time performance. The experimental results demonstrated that our proposed perception system achieved favorable accuracy, outputting the pose at 10 Hz, which is sufficient for online ego-motion estimation for combine harvesters.
自我运动估计是自主联合收割机的基础能力，支持导航和收割等高级功能。本文提出了一种从立体图像序列中估计联合收割机运动的新方法。该方法从立体匹配特征中三角化一组 3D 地标开始。通过最小化当前帧上这些地标重投影误差来获得六自由度（DoF）的自我运动。然后，在滑动窗口中联合执行局部捆绑调整以细化结构（即地标位置）和运动（即关键帧姿势）。这两个过程被封装到一个两线程架构中以实现实时性能。我们的方法利用立体相机，可以实现真实比例的估计和系统的轻松启动。在真实农业场景数据上进行了定量测试，包括几个不同的工作路径，以评估估计精度和实时性能。实验结果表明，我们提出的感知系统具有良好的准确性，能够以 10 Hz 的频率输出姿态，这足以满足联合收割机的在线自我运动估计。
