Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
6D SLAM—3D mapping outdoor environments: Research Articles
Journal of Field Robotics
Scale Space Histogram of Oriented Gradients for Human Detection
ISISE '08 Proceedings of the 2008 International Symposium on Information Science and Engieering - Volume 02
Automatic guidance of a four-wheel-steering mobile robot for accurate field operations
Journal of Field Robotics - Agricultural Robotics
Corn plant sensing using real-time stereo vision
Journal of Field Robotics - Agricultural Robotics
An autonomous rice transplanter guided by global positioning system and inertial measurement unit
Journal of Field Robotics - Agricultural Robotics
Development and implementation of a team of robotic tractors for autonomous peat moss harvesting
Journal of Field Robotics - Agricultural Robotics
Real-time hierarchical outdoor SLAM based on stereovision and GPS fusion
IEEE Transactions on Intelligent Transportation Systems
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
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Precision agricultural maps are required for agricultural machinery navigation, path planning and plantation supervision. In this work we present a Simultaneous Localization and Mapping (SLAM) 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.