Authors

Abstract

Conventional simultaneous localization and mapping (SLAM) systems for agricultural robots rely heavily on static rigidity assumptions, which makes it susceptible to the influence of dynamic target feature points in the environment thus leading to poor localization accuracy and robustness of the system. To address the above issues, this paper proposes a method that utilizes a target detection algorithm to identify and eliminate dynamic target feature points in a farm depot. The method initially employs the YOLOv5 target detection algorithm to recognize dynamic targets in the captured warehouse environment images. The detected targets are then integrated into the feature extraction process at the front end of the visual SLAM. Next, dynamic feature points belonging to the dynamic target part are eliminated from the extracted image feature points using the LK optical flow method. Finally, the remaining feature points are used for location matching, map construction and localization. The final test on the TUM dataset shows that the enhanced vision SLAM system improves the localization accuracy by 91.47% compared to ORB-SLAM2 in highly dynamic scenes. This improvement increases the accuracy and robustness of the system and outperforms some of the best SLAM algorithms while maintaining high real-time performance. These features make it more valuable for mobile devices.

Abstract in Chinese

农业机器人的传统同步定位和地图构建（SLAM）系统在很大程度上依赖于静态刚性假设，这使得它很容易受到环境中动态目标特征点的影响从而导致系统的定位精度和鲁棒性变差。针对上述问题，本文提出了一种利用目标检测算法来识别和消除农场库房中动态目标特征点的方法。该方法最初采用 YOLOv5 目标检测算法来识别采集库房环境图像中的动态目标。然后将检测到的目标整合到视觉 SLAM 前端的特征提取过程中。接着，使用 LK 光流方法从提取的图像特征点中剔除属于动态目标部分的动态特征点。最后，剩余的特征点用于位置匹配、地图构建和定位。在 TUM 数据集上的最终测试表明，在高动态场景中，增强型视觉 SLAM 系统与 ORB-SLAM2 相比，定位精度提高了 91.47%。这一改进提高了系统的准确性和鲁棒性，并在保持高实时性的同时超越了一些优秀的 SLAM 算法。这些特点使其对移动设备更有价值。