Real-time Monocular SLAM Research Articles

Real-time deformable SLAM with geometrically adapted template for dynamic monocular laparoscopic scenes.

Intraoperative reconstruction of endoscopic scenes is a key technology for surgical navigation systems. The accuracy and efficiency of 3D reconstruction directly determine the effectiveness of navigation systems in a variety of clinical applications. While current deformable SLAM algorithms can meet real-time requirements, their underlying reliance on regular templates still makes it challenging to efficiently capture abrupt geometric features within scenes, such as organ contours and surgical margins. We propose a novel real-time monocular deformable SLAM algorithm with geometrically adapted template. To ensure real-time performance, the proposed algorithm consists of two threads: a deformation mapping thread updates the template at keyframe rate and a deformation tracking thread estimates the camera pose and the deformation at frame rate. To capture geometric features more efficiently, the algorithm first detects salient edge features using a pre-trained contour detection network and then constructs the template through a triangulation method with guidance of the salient features. We thoroughly evaluated this method on Mandala and Hamlyn datasets in terms of accuracy and performance. The results demonstrated that the proposed method achieves better accuracy with 0.75-7.95% improvement and achieves consistent effectiveness in data association compared with the closest method. This study verified an adaptive template does improve the performance of reconstruction of dynamic laparoscopic Scenes with abrupt geometric features. However, further exploration is needed for applications in laparoscopic surgery with incisal margins caused by surgical instruments. This research serves as a crucial step toward enhanced automatic computer-assisted navigation in laparoscopic surgery. Code is available at https://github.com/Tang257/SLAM-with-geometrically-adapted-template .

IPR-VINS: Real-time monocular visual-inertial SLAM with implicit plane optimization

SLAM typically involves updating numerous landmarks to reconstruct scenes rather than generating a topological depiction of the actual environment. Planar features, prevalent in structured environments, can contribute to the development of more nuanced yet less cumbersome maps. The incorporation of fusions between feature points and planar facets gives rise to new discrepancies that must be meticulously addressed. To prevent the uncontrolled expansion of planar boundaries, a robust technique for boundary extraction is offered. Scene planes are fused using the boundary function, ensuring stable feature extraction and planar association. To leverage limited computational resources effectively, a streamlined back-end framework is constructed. Finally, factors are fine-tuned at a consistent scale, which not only elevates locational accuracy but also diminishes computational complexity. To reduce systematic errors, plane prior information is assimilated into global optimization. Compared with VINS-Mono, the average Absolute Trajectory Error of the proposed method on EuRoC is reduced by 44%.

A Real-Time Monocular Visual SLAM Based on the Bundle Adjustment with Adaptive Robust Kernel

A Real-Time Monocular Visual SLAM Based on the Bundle Adjustment with Adaptive Robust Kernel

Automatic real-time SLAM relocalization based on a hierarchical bipartite graph model

The need to increase the robustness of a real-time monocular SLAM system raises the important problem of relocalization; namely, how to automatically recover a SLAM system after tracking failures. We address this problem by proposing a real-time relocalization algorithm based on a hierarchical bipartite graph model. When the SLAM system is lost, we use the latter model to find sufficient correspondences between the detected image and stored map features, thus achieving efficient, real-time relocalization. The model accounts for both temporal and spatial constraints. Experimental results on both synthetic and real data support the effectiveness of the proposed algorithm.

Automatic Relocalization and Loop Closing for Real-Time Monocular SLAM

Monocular SLAM has the potential to turn inexpensive cameras into powerful pose sensors for applications such as robotics and augmented reality. We present a relocalization module for such systems which solves some of the problems encountered by previous monocular SLAM systems--tracking failure, map merging, and loop closure detection. This module extends recent advances in keypoint recognition to determine the camera pose relative to the landmarks within a single frame time of 33 ms. We first show how this module can be used to improve the robustness of these systems. Blur, sudden motion, and occlusion can all cause tracking to fail, leading to a corrupted map. Using the relocalization module, the system can automatically detect and recover from tracking failure while preserving map integrity. Extensive tests show that the system can then reliably generate maps for long sequences even in the presence of frequent tracking failure. We then show that the relocalization module can be used to recognize overlap in maps, i.e., when the camera has returned to a previously mapped area. Having established an overlap, we determine the relative pose of the maps using trajectory alignment so that independent maps can be merged and loop closure events can be recognized. The system combining all of these abilities is able to map larger environments and for significantly longer periods than previous systems.