Abstract
In this paper, we introduce a method to estimate the object’s pose from multiple cameras. We focus on direct estimation of the 3D object pose from 2D image sequences. Scale-Invariant Feature Transform (SIFT) is used to extract corresponding feature points from adjacent images in the video sequence. We first demonstrate that centralized pose estimation from the collection of corresponding feature points in the 2D images from all cameras can be obtained as a solution to a generalized Sylvester’s equation. We subsequently derive a distributed solution to pose estimation from multiple cameras and show that it is equivalent to the solution of the centralized pose estimation based on Sylvester’s equation. Specifically, we rely on collaboration among the multiple cameras to provide an iterative refinement of the independent solution to pose estimation obtained for each camera based on Sylvester’s equation. The proposed approach to pose estimation from multiple cameras relies on all of the information available from all cameras to obtain an estimate at each camera even when the image features are not visible to some of the cameras. The resulting pose estimation technique is therefore robust to occlusion and sensor errors from specific camera views. Moreover, the proposed approach does not require matching feature points among images from different camera views nor does it demand reconstruction of 3D points. Furthermore, the computational complexity of the proposed solution grows linearly with the number of cameras. Finally, computer simulation experiments demonstrate the accuracy and speed of our approach to pose estimation from multiple cameras.
Highlights
Object pose estimation from monocular or multiple views has been one of the most active research topics over the past few decades
We present a new approach to pose estimation from 2D image sequences from multiple cameras
Our approach provides a direct estimate of the 3D rotation parameters from 2D image sequences without constructing a 3D model or requiring system training and learning prior to pose estimation
Summary
Object pose estimation from monocular or multiple views has been one of the most active research topics over the past few decades. We provide an extremely efficient and robust solution to pose estimation from 2D image sequences from multiple cameras based on Sylvester’s equation. We extend our approach to pose estimation by forming the problem of centralized pose estimation from multiple cameras as a solution to a generalized Sylvester’s equation capturing all of the feature points from all cameras. We first present a centralized approach to pose estimation from 2D image sequences from multiple cameras as a solution to a generalized Sylvester’s equation.
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