Simultaneous Localization and Mapping (SLAM) is a process to use multiple sensors to position an unmanned mobile vehicle without previous knowledge of the environment, and meanwhile construct a map of this environment for the further applications. Over the past three decades, SLAM has been intensively researched and widely applied in mobile robot control and unmanned vehicle navigation. SLAM technology has demonstrated a great potential in autonomously navigating the mobile robot and simultaneously reconstructing the three-dimensional (3D) information of surrounding environment. With the vigorous driving of sensor technology and 3D reconstruction algorithms, many attempts have been conducted to propose novel systems and algorithms combined with different sensors to solve the SLAM problem. Notably, SLAM has been extended to various aspects of agriculture involved with autonomous navigation, 3D mapping, field monitoring, and intelligent spraying. This paper focuses on the recent developments and applications of SLAM, particularly in complex and unstructured agricultural environment.A detailed summary of the developments of SLAM is given from three main fundamental types: light detection and ranging SLAM, Visual SLAM, and Sensor Fusion SLAM, and we also discuss the applications and prospects of SLAM technology in agricultural mapping, agricultural navigation, and precise automatic agriculture. Particular attention has been paid to the SLAM sensors, systems, and algorithms applied in agricultural tasks. Additionally, the challenges and future trends of SLAM are reported.
Contact stiffness increases with brake pressure, which leads to an increase of the mode frequency of the brake disc.
As the disc mode order increases, the contact stiffness first increases and then almost stays invariant, which makes a mode frequency change correspondingly. The dependence of contact stiffness on brake pressure and disc mode order could be one of the possible reasons why the current research on prediction of brake noise and vibration is inaccurate and unsatisfactory.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.