  Accurate camera pose estimation result is essential for visual SLAM (VSLAM).
This paper presents a novel pose correction method to improve the accuracy of
the VSLAM system. Firstly, the relationship between the camera pose estimation
error and bias values of map points is derived based on the optimized function
in VSLAM. Secondly, the bias value of the map point is calculated by a
statistical method. Finally, the camera pose estimation error is compensated
according to the first derived relationship. After the pose correction,
procedures of the original system, such as the bundle adjustment (BA)
optimization, can be executed as before. Compared with existing methods, our
algorithm is compact and effective and can be easily generalized to different
VSLAM systems. Additionally, the robustness to system noise of our method is
better than feature selection methods, due to all original system information
is preserved in our algorithm while only a subset is employed in the latter.
Experimental results on benchmark datasets show that our approach leads to
considerable improvements over state-of-the-art algorithms for absolute pose
estimation.
