Optimized EIF-SLAM algorithm for precision agriculture mapping based on stems detection
Optimized EIF-SLAM algorithm for precision agriculture mapping based on stems detection
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DOI：
10.1016/j.compag.2011.07.007
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发表时间：
2011-09-01
影响因子：
8.3
通讯作者：
Carelli, R.
中科院分区：
文献类型：
--
作者：
Auat Cheein, F.;Steiner, G.;Carelli, R.
农业机械导航、路径规划和种植园监督都需要精确的农业地图。在这项工作中，我们提出了一个同时定位和地图（SIAM）算法解决了扩展信息过滤器（EIF）的农业环境（格罗夫斯）。SLAM算法在一个无人驾驶的非完整类车移动的机器人上实现。环境地图基于对种植园橄榄茎的检测。橄榄茎是通过两种方式获得的：距离传感器激光和单目视觉系统。支持向量机（SVM）的视觉系统上实现检测从环境中获取的图像上的橄榄茎。此外，SLAM算法具有与之相关联的优化准则。该优化准则基于仅使用从估计收敛角度来看从环境信息提取的最有意义的茎来校正SLAM系统状态向量，而不损害估计一致性。优化标准，其示范和实验结果在真实的农业环境中显示我们的建议的性能也包括在这项工作中。(C)2011 Elsevier B.V.保留所有权利。
Precision agricultural maps are required for agricultural machinery navigation, path planning and plantation supervision. In this work we present a Simultaneous Localization and Mapping (SIAM) algorithm solved by an Extended Information Filter (EIF) for agricultural environments (olive groves). The SLAM algorithm is implemented on an unmanned non-holonomic car-like mobile robot. The map of the environment is based on the detection of olive stems from the plantation. The olive stems are acquired by means of both: a range sensor laser and a monocular vision system. A support vector machine (SVM) is implemented on the vision system to detect olive stems on the images acquired from the environment. Also, the SLAM algorithm has an optimization criterion associated with it. This optimization criterion is based on the correction of the SLAM system state vector using only the most meaningful stems - from an estimation convergence perspective - extracted from the environment information without compromising the estimation consistency. The optimization criterion, its demonstration and experimental results within real agricultural environments showing the performance of our proposal are also included in this work. (C) 2011 Elsevier B.V. All rights reserved.