Exact(10)
An iterative closest point (ICP) algorithm [56] have widely been used to estimate camera motion.
It is obviously difficult to estimate camera poses of each row directly.
After the initialization, tracking and mapping are performed to continuously estimate camera poses.
In the tracking process, 3D ray information is also used to estimate camera motion.
Mei et al. used an old vSLAM map to estimate camera motion at run-time in see-through.
Typically, this class of algorithms first estimates approximate 3D coordinates from a stereo image pair and then links up feature tracks over multiple pairs to estimate camera motion.
Similar(50)
This algorithm uses the AprilTag to estimate cameras' poses and employ a nonlinear optimization method to refine these poses when multiple tags are in the field of view.
The proposed method is able to lower the load on estimating camera position while losing very little precision.
Lv et al. [8, 9] proposed a self-calibration method for estimating camera's intrinsic and extrinsic parameters.
They are extracted by estimating camera motion for each frame from optical flows and cancelling it before feature description.
This means that the tracking estimates camera motion in real-time, and the mapping estimates accurate 3D positions of feature points with a computational cost.
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