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 ne-tuned baselines in both accuracy and real-time.

Disparity upsampling methods are derived for restoring high-quality disparity maps from active three-dimensional imaging techniques such as time-of-flight method. Although effective, traditional upsampling methods exhibit certain drawbacks. For example, noisy disparity values in the low-scaled level are taken indiscriminately to carry out the upsampling assignment, which could lead to dramatically weakened results. To solve this problem, we herein present a Markov random field (MRF) based disparity upsampling method using confidence evaluations. We utilize confidence measures under the stereo configuration to evaluate the reliability of disparity value. The confidence maps are then properly integrated into the state-of-the-art MRF-based depth upsampling (MBU) method to constrain the negative effect caused by the noisy disparity. More specifically, the confidence is used to remove unqualified pixels from participating in the disparity upsampling computation. Extensive experiments were performed to validate the proposed new method. In these experiments, initial low-scaled disparity maps were from either ground truth or stereo matching methods. Results verified that our method can obtain significantly better upsampled disparity maps than that from the original MBU or other nonconfidence-based methods. In addition, we implemented our algorithms on general public utilities to improve the execution speed.

An augmented reality (AR) technique has recently gained its popularity in minimally invasive surgery. Tracking is a crucial step to achieve precise AR. Besides optical tracking in traditional medical AR, visual tracking attracts a lot of attention due to its generality. Moreover, when the target organ's 3-D model can be obtained from preoperative images and under the model rigidity assumption, tracking is then converted into a problem of computing the six-degree-of-freedom pose of the 3-D model. In this paper, we introduce a robust tracking algorithm in our endoscopic AR system, where we combine the benefits of both region and dense cues in a unified framework. Each kind of cues alone may not be adequate for tracking in endoscopic surgery. However, they have complementary characteristics, with region cues being more robust to motion blur and fast motion, and dense cues being more accurate when motion is not large. We also propose an appearance model adaption method and an occlusion processing method to effectively handle occlusions. Experiments on both synthetic dataset and simulated surgical environment show the effectiveness and robustness of our proposed method. This work presents a novel tracking strategy in medical AR applications.