Search results
Results from the WOW.Com Content Network
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 ...
Simultaneous object localization and classification is benchmarked by the mean average precision (mAP). The average precision (AP) of the network for a class of objects is the area under the precision-recall curve as the IoU threshold is varied. The mAP is the average of AP over all classes.
Allows the phone to understand and track its position relative to the world.; A motion tracking process known as simultaneous localization and mapping (SLAM) utilizes feature points - which are visually distinct objects within camera view - to provide focal points for the phone to determine proper positioning (pose) of the device.
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 ...