  Initialization is essential to monocular Simultaneous Localization and
Mapping (SLAM) problems. This paper focuses on a novel initialization method
for monocular SLAM based on planar features. The algorithm starts by homography
estimation in a sliding window. It then proceeds to a global plane optimization
(GPO) to obtain camera poses and the plane normal. 3D points can be recovered
using planar constraints without triangulation. The proposed method fully
exploits the plane information from multiple frames and avoids the ambiguities
in homography decomposition. We validate our algorithm on the collected
chessboard dataset against baseline implementations and present extensive
analysis. Experimental results show that our method outperforms the fine-tuned
baselines in both accuracy and real-time.
