"Simultaneous localization and mapping (SLAM) is the computational problem of constructing or updating a map of an unknown environment while simultaneously keeping track of an agent's location within it."
Techniques used for robot localization, mapping, and navigation.
Sensor technologies: The types, advantages and limitations of sensors used in robotics for localization, mapping and navigation.
Localization: The process of determining the robot's position and orientation within a given environment using sensor data.
Mapping: The process of creating a map of the environment using sensor data.
SLAM (Simultaneous Localization and Mapping): A technique that enables a robot to map an unknown environment while simultaneously determining its own position within that environment.
Odometry: The process of using the robot's wheel rotations to estimate its position and movement.
Path planning: The process of finding a suitable path for the robot to move from one point to another while avoiding obstacles.
Control systems: The algorithms and methods used to control the robot's motion based on sensor data and path planning.
Robot kinematics: The study of the motion and geometry of robots, including their joints, linkages and end-effectors.
State estimation: The process of estimating the state of the robot's environment, including the position and orientation of objects within it.
Simulations: The use of software tools to simulate the behavior of robots in various environments.
Machine learning: The use of algorithms to improve the performance of robots in tasks such as localization, mapping and navigation.
Artificial intelligence: The use of intelligent algorithms to enable robots to make decisions and act autonomously.
Communication technologies: The protocols and methods used to exchange data between robots and other devices in their environment.
Mobile robot platforms: The types of vehicles used for robotics applications, including wheeled, tracked, or legged robots.
Human-robot interaction: The study of how robots can interact with humans in safe and effective ways.
Geometric Localization: It involves finding the robot's current position and orientation based on geometric features in the environment.
Feature-Based Localization: It relies on identifying specific features or landmarks in the environment, such as corners or edges, to determine the robot's position.
Monte Carlo Localization: This type of localization uses probability distributions to determine the robot's position, making it a probabilistic approach.
Simultaneous Localization and Mapping (SLAM): This technique involves building a map of the environment while simultaneously localizing the robot's position within that map.
Visual Localization: It involves using cameras or other visual sensors to identify objects in the environment and determine the robot's position based on their relative position to those objects.
Beacon Localization: It involves using beacons or markers placed in the environment to help the robot determine its position and orientation.
Radio Frequency Identification (RFID) Localization: It uses RFID tags placed in the environment to help the robot navigate and accurately locate itself.
Magnetic Localization: It relies on the detection of magnetic fields in the environment to determine the robot's location.
Inertial Navigation: It uses inertial sensors such as accelerometers and gyroscopes to determine the robot's position and orientation.
Iterative Closest Point (ICP) Localization: It involves matching a point cloud generated by a 3D sensor to a pre-existing map to determine the robot's position.
Semantic Mapping: It involves building a map of the environment that includes information about the semantic meaning of objects, such as the purpose or function of the different areas.
Grid-Based Localization: It divides the environment into a grid and uses data from sensors to determine the robot's position and orientation within that grid.
"Popular approximate solution methods include the particle filter, extended Kalman filter, covariance intersection, and GraphSLAM."
"SLAM algorithms are based on concepts in computational geometry and computer vision, and are used in robot navigation, robotic mapping, and odometry for virtual reality or augmented reality."
"SLAM algorithms are tailored to the available resources and are not aimed at perfection but at operational compliance."
"Published approaches are employed in self-driving cars, unmanned aerial vehicles, autonomous underwater vehicles, planetary rovers, newer domestic robots and even inside the human body."
"While this initially appears to be a chicken or the egg problem, there are several algorithms known to solve it in, at least approximately, tractable time for certain environments."
"Popular approximate solution methods include the particle filter, extended Kalman filter, covariance intersection, and GraphSLAM."
"SLAM algorithms are based on concepts in computational geometry and computer vision."
"SLAM algorithms enable constructing or updating a map of an unknown environment while simultaneously keeping track of an agent's location within it."
"SLAM algorithms are tailored to the available resources and are not aimed at perfection but at operational compliance."
"SLAM algorithms are used in robot navigation, robotic mapping, and odometry for virtual reality or augmented reality."
"Published approaches are employed in self-driving cars, unmanned aerial vehicles, autonomous underwater vehicles, planetary rovers, newer domestic robots and even inside the human body."
"While this initially appears to be a chicken or the egg problem, there are several algorithms known to solve it in, at least approximately, tractable time for certain environments."
"Published approaches are employed in self-driving cars, unmanned aerial vehicles, autonomous underwater vehicles, planetary rovers, newer domestic robots and even inside the human body."
"SLAM algorithms are based on concepts in computational geometry and computer vision."
"SLAM algorithms are tailored to the available resources and are not aimed at perfection but at operational compliance."
"SLAM algorithms are used in robot navigation, robotic mapping, and odometry for virtual reality or augmented reality."
"Published approaches are employed in self-driving cars, unmanned aerial vehicles, autonomous underwater vehicles, planetary rovers, newer domestic robots and even inside the human body."
"There are several algorithms known to solve it in, at least approximately, tractable time for certain environments."
"Popular approximate solution methods include the particle filter, extended Kalman filter, covariance intersection, and GraphSLAM."