Autonomous navigation in greenhouses requires agricultural robots to localize and generate a globally consistent map of surroundings in real-time. However, accurate and robust localization and mapping are still challenging for agricultural robots due to the unstructured, dynamic and GPS-denied environmental conditions. In this study, a state-of-the-art real-time localization and mapping system was presented to achieve precise pose estimation and dense three-dimensional (3D) point cloud mapping in complex greenhouses by utilizing multi-sensor fusion and Visual–IMU–Wheel odometry. In this method, measurements from wheel odometry, an inertial measurement unit (IMU) and a tightly coupled visual–inertial odometry (VIO) are integrated into a loosely coupled framework based on the Extended Kalman Filter (EKF) to obtain a more accurate state estimation of the robot. In the multi-sensor fusion algorithm, the pose estimations from the wheel odometry and IMU are treated as predictions and the localization results from VIO are used as observations to update the state vector. Simultaneously, the dense 3D map of the greenhouse is reconstructed in real-time by employing the modified ORB-SLAM2. The performance of the proposed system was evaluated in modern standard solar greenhouses with harsh environmental conditions. Taking advantage of measurements from individual sensors, our method is robust enough to cope with various challenges, as shown by extensive experiments conducted in the greenhouses and outdoor campus environment. Additionally, the results show that our proposed framework can improve the localization accuracy of the visual–inertial odometry, demonstrating the satisfactory capability of the proposed approach and highlighting its promising applications in autonomous navigation of agricultural robots.

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Real-Time Localization and Mapping Utilizing Multi-Sensor Fusion and Visual–IMU–Wheel Odometry for Agricultural Robots in Unstructured, Dynamic and GPS-Denied Greenhouse Environments

An evolvable adversarial network with gradient penalty for COVID-19 infection segmentation.

The assumption of a static environment is typical in many visual simultaneous localization and mapping (VSLAM) systems. However dynamic objects in open scenes will mislead feature association and even fail to match, which reduces the accuracy of localization. For dynamic scenarios, a robust visual SLAM system that utilizes weighted features, namely, named WF-SLAM is proposed in this paper, which is based on ORB-SLAM2. First, WF-SLAM applies the tightly coupled semantic and geometric dynamic target detection algorithm to obtain the dynamic information in the scene. Then, WF-SLAM defines feature point weights and initializes them with the dynamic information. Finally, the pose optimization in ORB-SLAM2 is changed to weight-based joint optimization. WF-SLAM significantly decreases mismatch and improves the accuracy of localization. Experiments are performed on the benchmark RGB-D dataset TUM and real-world scenarios, and the results demonstrate that WF-SLAM realizes significant improvements in term of localization accuracy compared to ORB-SLAM2 in dynamic environments and a more robust performance compared with state-of-the-art dynamic SLAM methods.

WF-SLAM: A Robust VSLAM for Dynamic Scenarios via Weighted Features

Background Indoor navigation within closed facilities has been subject of studies with different application areas, particularly in recent years (e.g. the navigation requirements of people or the location of objects). Hospitals are of specific interest in this regard as the multitude of technical equipment used is potentially interfering with navigation systems. Objective This research examines relevant studies regarding Indoor Positioning Systems (IPS) in hospitals and IPS that are designed for hospitals and in preparation for implementation, by investigating the respective technologies, techniques, prediction-improving methods, evaluation results, and limitations of the IPS. Methods To gather current and future IPS in hospitals, the methodology of a Scoping Review was used. The study has been conducted by applying the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Framework to the context of IPS in hospitals. The results and limitations concerning current and future IPS in hospitals were gathered and structured by using a highly cited evaluation framework for IPS. Results Thirty-eight studies were considered for this research. The IPS technologies investigated were Bluetooth Low Energy (n = 17), Wireless-Fidelity (n = 10), Hybrids (n = 4), Radio-Frequency Identification (n = 4), Ultra-Wideband (n = 1), Infrared (n = 1) and ZigBee (n = 1). Conclusions This study presents current and future IPS in hospitals. For future IPS research and IPS in hospitals, the theoretical implications contribute to our knowledge about IPS technologies, techniques, prediction-improving methods, evaluation results and limitations during testing/implementing IPS in hospitals. As practical implications, the insights of this study can be used by developers to improve IPS and by hospitals to facilitate IPS implementation.

Indoor positioning systems in hospitals: A scoping review

ADR-MVSNet: A cascade network for 3D point cloud reconstruction with pixel occlusion

Visual SLAM for robot navigation in healthcare facility

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Visual SLAM for robot navigation in healthcare facility