Simultaneous localization and mapping (SLAM) methods provide real-time estimation of 3-D models from the sole input of a handheld camera, routinely in mobile robotics scenarios. Medical endoscopic sequences mimic a robotic scenario in which a handheld camera (monocular endoscope) moves along an unknown trajectory while observing an unknown cavity. However, the feasibility and accuracy of SLAM methods have not been extensively validated with human in vivo image sequences. In this work, we propose a monocular visual SLAM algorithm tailored to deal with medical image sequences in order to provide an up-to-scale 3-D map of the observed cavity and the endoscope trajectory at frame rate. The algorithm is validated over synthetic data and human in vivo sequences corresponding to 15 laparoscopic hernioplasties where accurate ground-truth distances are available. It can be concluded that the proposed procedure is: 1) noninvasive, because only a standard monocular endoscope and a surgical tool are used; 2) convenient, because only a hand-controlled exploratory motion is needed; 3) fast, because the algorithm provides the 3-D map and the trajectory in real time; 4) accurate, because it has been validated with respect to ground-truth; and 5) robust to inter-patient variability, because it has performed successfully over the validation sequences.

In recent years, research on visual SLAM has produced robust algorithms providing, in real time at 30 Hz, both the 3D model of the observed rigid scene and the 3D camera motion using as only input the gathered image sequence. These algorithms have been extensively validated in rigid human-made environments -indoor and outdoor- showing robust performance in dealing with clutter, occlusions or sudden motions. Medical endoscopic sequences naturally pose a monocular SLAM problem: an unknown camera motion in an unknown environment. The corresponding map would be useful in providing 3D information to assist surgeons, to support augmented reality insertions or to be exploited by medical robots. In this paper we propose the combination EKF Monocular SLAM + 1-Point RANSAC + Randomised List Relocalization to process laparoscopic sequences -abdominal cavity images-. The sequences are challenging due to: 1) cluttering produced by tools; 2) sudden motions of the camera; 3) laparoscope frequently goes in and out of abdominal cavity; 4) tissue deformation caused by respiration, heartbeats and/or surgical tools. Real medical image sequences provide experimental validation.

Recently, classical pairwise Structure From Motion (SfM) techniques have been combined with non-linear global optimization (Bundle Adjustment, BA) over a sliding window to recursively provide camera pose and feature location estimation from long image sequences. Normally called Visual Odometry, these algorithms are nowadays able to estimate with impressive accuracy trajectories of hundreds of meters; either from an image sequence (usually stereo) as the only input, or combining visual and propioceptive information from inertial sensors or wheel odometry. This paper has a double objective. First, we aim to illustrate for the first time how similar accuracy and trajectory length can be achieved by filtering-based visual SLAM methods. Specifically, a camera-centered Extended Kalman Filter is used here to process a monocular sequence as the only input, with 6DOF motion estimated. Features are kept live in the filter while visible as the camera explores forward, and are deleted from the state once they go out of view. This permits an increase in the number of tracked features per frame from tens to around a hundred. While improving the accuracy of the estimation, it makes computationally infeasible the exhaustive Branch and Bound search performed by standard JCBB for match outlier rejection. As a second contribution that overcomes this problem, we present here a RANSAC-like algorithm that exploits the probabilistic prediction of the filter. This use of prior information makes it possible to reduce the size of the minimal data subset to instantiate a hypothesis to the minimum possible of 1 point, greatly increasing the efficiency of the outlier rejection stage. Experimental results from real image sequences covering trajectories of hundreds of meters are presented and compared against RTK GPS ground truth. Estimation errors are about 1% of the trajectory for trajectories up to 650 metres.