Colorbot download4/30/2023 ![]() Improving MRI image quality and resolution thus becomes a critically important task. On the other hand, accurate diagnosis by MRI remains a great challenge as images obtained via present MRI techniques usually have low resolutions. Magnetic Resonance Imaging(MRI) has been widely used in clinical application and pathology research by helping doctors make more accurate diagnoses. Experiments show UAPF can achieve robot kidnap recovery in less than 2 s and position error is less than 0.1 m in a hall of 40 by 15 m, when the currently prevalent lidar-based localization costs more than 90 s and converges to wrong position. To achieve more accurate ranging-based localization, linear regression of ranging data adopts values of maximum probability rather than average distances. Besides, pose fusion of ranging-based localization and PF-based localization is conducted to decrease the uncertainty. UAPF adaptively updates the covariance by Jacobian from Ultra-wide Band information instead of predetermined parameters, and determines whether robot kidnap occurs by a novel criterion called KNP (Kidnap Probability). To address this problem, an improved Particle Filter localization is proposed who could achieve robust robot kidnap detection and pose error compensation. Lidar-based localization doesn’t have high accuracy in open scenarios with few features, and behaves poorly in robot kidnap recovery. Our system has been successfully tested in real domestic environments. This system has demonstrated to support autonomous navigation based on language interaction and significantly improve the safety, efficiency, and robustness of indoor robot navigation. The path planning algorithm is carried on respectively in every room, which significantly improves the navigation efficiency. Furthermore, based on the topological map and auxiliary navigation points, the global path is segmented into each topological area. To improve the navigation success rate and safety, we generate auxiliary navigation points on both sides of the door to correct the robot trajectory. Then, the robot selects the goal point from the target space by object affordance theory. First, natural language is used as a human–robot interaction form, from which the target room, object, and spatial relationship can be extracted by using speech recognition and word segmentation. This paper proposes an indoor navigation system based on object semantic grid and topological map, to optimize the above problems. The performance of SLAM sensors is compared using analytical hierarchy process (AHP) based on various key indicators such as accuracy, range, cost, working environment and computational cost.įor the indoor navigation of service robots, human–robot interaction and adapting to the environment still need to be strengthened, including determining the navigation goal socially, improving the success rate of passing doors, and optimizing the path planning efficiency. In this article, a detailed literature review of widely used SLAM sensor such as acoustic sensor, RADAR, camera, Light Detection and Ranging (LiDAR), and RGB-D is provided. Therefore, understanding the operational limits of the available SLAM sensors and data collection techniques from a single or multi-sensors is noteworthy. These sensors play significant role in acquiring accurate environmental information for further processing and mapping. SLAM consists of front and back-end processes, wherein, the front-end includes SLAM sensors. ![]() Simultaneous localization and mapping (SLAM) is one of the widely used techniques in mobile robots for localization and navigation. ![]() Accurate navigation of a mobile robot is highly significant for its uninterrupted operation. Applications of mobile robots are continuously capturing the importance in numerous areas such as agriculture, surveillance, defense and planetary exploration to name a few.
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