BY-SLAM: Dynamic Visual SLAM System Based on BEBLID and Semantic Information Extraction.

SLAM is a critical technology for enabling autonomous navigation and positioning in unmanned vehicles. Traditional visual simultaneous localization and mapping algorithms are built upon the assumption of a static scene, overlooking the impact of dynamic targets within real-world environments. Interference from dynamic targets can significantly degrade the system's localization accuracy or even lead to tracking failure. To address these issues, we propose a dynamic visual SLAM system named BY-SLAM, which is based on BEBLID and semantic information extraction. Initially, the BEBLID descriptor is introduced to describe Oriented FAST feature points, enhancing both feature point matching accuracy and speed. Subsequently, FasterNet replaces the backbone network of YOLOv8s to expedite semantic information extraction. By using the results of DBSCAN clustering object detection, a more refined semantic mask is obtained. Finally, by leveraging the semantic mask and epipolar constraints, dynamic feature points are discerned and eliminated, allowing for the utilization of only static feature points for pose estimation and the construction of a dense 3D map that excludes dynamic targets. Experimental evaluations are conducted on both the TUM RGB-D dataset and real-world scenarios and demonstrate the effectiveness of the proposed algorithm at filtering out dynamic targets within the scenes. On average, the localization accuracy for the TUM RGB-D dataset improves by 95.53% compared to ORB-SLAM3. Comparative analyses against classical dynamic SLAM systems further corroborate the improvement in localization accuracy, map readability, and robustness achieved by BY-SLAM.

RC-SLAM: Road Constrained Stereo Visual SLAM System Based on Graph Optimization.

Intelligent vehicles are constrained by road, resulting in a disparity between the assumed six degrees of freedom (DoF) motion within the Visual Simultaneous Localization and Mapping (SLAM) system and the approximate planar motion of vehicles in local areas, inevitably causing additional pose estimation errors. To address this problem, a stereo Visual SLAM system with road constraints based on graph optimization is proposed, called RC-SLAM. Addressing the challenge of representing roads parametrically, a novel method is proposed to approximate local roads as discrete planes and extract parameters of local road planes (LRPs) using homography. Unlike conventional methods, constraints between the vehicle and LRPs are established, effectively mitigating errors arising from assumed six DoF motion in the system. Furthermore, to avoid the impact of depth uncertainty in road features, epipolar constraints are employed to estimate rotation by minimizing the distance between road feature points and epipolar lines, robust rotation estimation is achieved despite depth uncertainties. Notably, a distinctive nonlinear optimization model based on graph optimization is presented, jointly optimizing the poses of vehicle trajectories, LPRs, and map points. The experiments on two datasets demonstrate that the proposed system achieved more accurate estimations of vehicle trajectories by introducing constraints between the vehicle and LRPs. The experiments on a real-world dataset further validate the effectiveness of the proposed system.

A Novel Water-Shore-Line Detection Method for USV Autonomous Navigation.

For the navigation of an unmanned surface vehicle (USV), detection and recognition of the water-shore-line (WSL) is an important part of its intellectualization. Current research on this issue mainly focuses on the straight WSL obtained by straight line fitting. However, the WSL in the image acquired by boat-borne vision is not always in a straight line, especially in an inland river waterway. In this paper, a novel three-step approach for WSL detection is therefore proposed to solve this problem through the information of an image sequence. Firstly, the initial line segment pool is built by the line segment detector (LSD) algorithm. Then, the coarse-to-fine strategy is used to obtain the onshore line segment pool, including the rough selection of water area instability and the fine selection of the epipolar constraint between image frames, both of which are demonstrated in detail in the text. Finally, the complete shore area is generated by an onshore line segment pool of multi-frame images, and the lower boundary of the area is the desired WSL. In order to verify the accuracy and robustness of the proposed method, field experiments were carried out in the inland river scene. Compared with other detection algorithms based on image processing, the results demonstrate that this method is more adaptable, and can detect not only the straight WSL, but also the curved WSL.

Stereo vision-based tracking of soft tissue motion with application to online ablation control in laser microsurgery.

Recent research has revealed that image-based methods can enhance accuracy and safety in laser microsurgery. In this study, non-rigid tracking using surgical stereo imaging and its application to laser ablation is discussed. A recently developed motion estimation framework based on piecewise affine deformation modeling is extended by a mesh refinement step and considering texture information. This compensates for tracking inaccuracies potentially caused by inconsistent feature matches or drift. To facilitate online application of the method, computational load is reduced by concurrent processing and affine-invariant fusion of tracking and refinement results. The residual latency-dependent tracking error is further minimized by Kalman filter-based upsampling, considering a motion model in disparity space. Accuracy is assessed in laparoscopic, beating heart, and laryngeal sequences with challenging conditions, such as partial occlusions and significant deformation. Performance is compared with that of state-of-the-art methods. In addition, the online capability of the method is evaluated by tracking two motion patterns performed by a high-precision parallel-kinematic platform. Related experiments are discussed for tissue substitute and porcine soft tissue in order to compare performances in an ideal scenario and in a setup mimicking clinical conditions. Regarding the soft tissue trial, the tracking error can be significantly reduced from 0.72 mm to below 0.05 mm with mesh refinement. To demonstrate online laser path adaptation during ablation, the non-rigid tracking framework is integrated into a setup consisting of a surgical Er:YAG laser, a three-axis scanning unit, and a low-noise stereo camera. Regardless of the error source, such as laser-to-camera registration, camera calibration, image-based tracking, and scanning latency, the ablation root mean square error is kept below 0.21 mm when the sample moves according to the aforementioned patterns. Final experiments regarding motion-compensated laser ablation of structurally deforming tissue highlight the potential of the method for vision-guided laser surgery.

Soft tissue motion tracking with application to tablet-based incision planning in laser surgery.

PURPOSE: Recent research has revealed that incision planning in laser surgery deploying stylus and tablet outperforms micromanipulator control. However, vision-based adaption to dynamic surgical scenes has not been addressed so far. In this study, scene motion compensation for tablet-based planning by means of tissue deformation tracking is discussed. METHODS: A stereo-based method for motion tracking with piecewise affine deformation modeling is presented. Proposed parametrization relies on the epipolar constraint to enforce left-right consistency in the energy minimization problem. Furthermore, the method implements illumination-invariant tracking and appearance-based occlusion detection. Performance is assessed on laparoscopic and laryngeal in vivo data. In particular, tracking accuracy is measured under various conditions such as occlusions and simulated laser cuttings. Experimental validation is extended to a user study conducted on a tablet-based interface that integrates the tracking for image stabilization. RESULTS: Tracking accuracy measurement reveals a root-mean-square error of 2.45 mm for the laparoscopic and 0.41 mm for the laryngeal dataset. Results successfully demonstrate stereoscopic tracking under changes in illumination, translation, rotation and scale. In particular, proposed occlusion detection scheme can increase robustness against tracking failure. Moreover, assessed user performance indicates significantly increased path tracing accuracy and usability if proposed tracking is deployed to stabilize the view during free-hand path definition. CONCLUSION: The presented algorithm successfully extends piecewise affine deformation tracking to stereo vision taking the epipolar constraint into account. Improved surgical performance as demonstrated for laser incision planning highlights the potential of presented method regarding further applications in computer-assisted surgery.