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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.
This is a list of simultaneous localization and mapping (SLAM) methods. The KITTI Vision Benchmark Suite website has a more comprehensive list of Visual SLAM methods.
Map learning cannot be separated from the localization process, and a difficulty arises when errors in localization are incorporated into the map. This problem is commonly referred to as Simultaneous localization and mapping (SLAM).
Robot localization denotes the robot's ability to establish its own position and orientation within the frame of reference. Path planning is effectively an extension of localization, in that it requires the determination of the robot's current position and a position of a goal location, both within the same frame of reference or coordinates ...
Given 3D point = (,,) with world coordinates in a reference frame (,,), observed from different views, the inverse depth parametrization of is given by: = (,,,,,) where the first five components encode the camera pose in the first observation of the point, being = (,,) the optical centre, the azimuth, the elevation angle, and = ‖ ‖ the inverse depth of at the first observation.
The Mobile Robot Programming Toolkit (MRPT) is a cross-platform software C++ library for helping robotics researchers design and implement algorithms related to simultaneous localization and mapping (SLAM), computer vision, and motion planning (obstacle avoidance). Different research groups have employed MRPT to implement projects reported in ...
The problem of simultaneous localization and mapping also fits the framework of invariant extended Kalman filtering after embedding of the state (consisting of attitude matrix , position vector and a sequence of static feature points , …,) into the Lie group + (or + for planar systems) [8] defined by the group operation:
Another type of pose tracking used more often in newer systems is referred to as inside-out tracking, including Simultaneous localization and mapping (SLAM) or Visual-inertial odometry (VIO). One example of a device that uses inside-out pose tracking is the Oculus Quest 2.