LA-SLAM for Laparoscopic Navigation With Pose Estimation and 3-D Surface Reconstruction

Laparoscopic navigation systems increasingly leverage augmented reality (AR) to overlay preoperative anatomical data in real time, enhancing surgical precision. However, reliable navigation requires accurate camera pose estimation and highfidelity 3D organ reconstruction. To address these challenges, we propose LA-SLAM, a real-time visual SLAM system specifically designed for laparoscopic surgery. The system incorporates three key innovations: (1) An optical-flow-based depth-pose joint estimation module. This module establishes accurate dense correspondences between images through the introduced global correlation softmax, providing sufficiently strong constraints for subsequent optimization to achieve high-precision pose and depth estimation with only a single update, significantly improving computational efficiency; (2) a hybrid loop closure strategy that integrates projection flow and dense optical flow for reliable detection, and introduces a Sim(3)-based optimization using 3D point cloud feature matching; and (3) a refined 3D reconstruction strategy combining depth consistency validation with statistical and gradient-based filtering to address laparoscopic challenges, including specular reflections, occlusions, and tissue deformation. Evaluations on standard SLAM datasets (TUM-RGBD, EuRoC) and laparoscopic datasets (SCARED, DePoLL, Stereo-MIS) demonstrate that LA-SLAM achieves tracking accuracy comparable to state-of-the-art SLAM systems while maintaining significant advantages in computational efficiency. It also produces geometrically faithful and visually robust reconstructions. These results highlight LA-SLAM’s potential for integration into real-time surgical navigation systems.