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Simultaneous Localization and Mapping (SLAM) enables mobile robots to autonomously perform localization and mapping tasks in unknown environments. Despite significant progress achieved by visual SLAM systems in ideal conditions, relying solely on a single robot and point features for mapping in large-scale indoor environments with weak-texture structures can affect mapping efficiency and accuracy. Therefore, this paper proposes a multi-robot collaborative mapping method based on point-line fusion to address this issue. This method is designed for indoor environments with weak-texture structures for localization and mapping. The feature-extraction algorithm, which combines point and line features, supplements the existing environment point feature-extraction method by introducing a line feature-extraction step. This integration ensures the accuracy of visual odometry estimation in scenes with pronounced weak-texture structure features. For relatively large indoor scenes, a scene-recognition-based map-fusion method is proposed in this paper to enhance mapping efficiency. This method relies on visual bag of words to determine overlapping areas in the scene, while also proposing a keyframe-extraction method based on photogrammetry to improve the algorithm's robustness. By combining the Perspective-3-Point (P3P) algorithm and Bundle Adjustment (BA) algorithm, the relative pose-transformation relationships of multi-robots in overlapping scenes are resolved, and map fusion is performed based on these relative pose relationships. We evaluated our algorithm on public datasets and a mobile robot platform. The experimental results demonstrate that the proposed algorithm exhibits higher robustness and mapping accuracy. It shows significant effectiveness in handling mapping in scenarios with weak texture and structure, as well as in small-scale map fusion. [ABSTRACT FROM AUTHOR]

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With the development of intelligent era, the application of UAVs is more and more extensive, and positioning and navigation technology is the key. Among them, the monocular VIO algorithm, with its advantages of lightweight and low cost, is the research focus of scholars today. However, VIO algorithm based on point features is easy to fail to locate in environments with weak texture, because it cannot extract enough corner features. In this paper, we propose a visual-inertial odometry based on point and line features, which is a tightly-coupled monocular one optimized in real time. In order to improve the positioning accuracy in the low light environments, we propose an image preprocessing algorithm based on adaptive strategy to increase the number of features detected. In order to reduce the difficulty of line feature matching, the method of grouping first and then merging is used to merge the wrongly segmented long line segment features. This also reduces the chasm of line features. At the same time, in the initialization procedure, a least square problem is used to estimate the acceleration bias to improve accuracy in localization. Experiments in the benchmark EuRoc dataset show that under the same operating environments and experimental parameter settings, our algorithm has higher accuracy and better robustness in localization than several mainstream visual-inertial odometry algorithms that only use point features. At the same time, compared with PLF-VINS, IPL-VINS and PL-VINS, the error in localization of our algorithm is reduced by about 0.6%, 18.9% and 9.7%. [ABSTRACT FROM AUTHOR]

Simultaneous Localization and Mapping (SLAM) enables mobile robots to autonomously perform localization and mapping tasks in unknown environments. Despite significant progress achieved by visual SLAM systems in ideal conditions, relying solely on a single robot and point features for mapping in large-scale indoor environments with weak-texture structures can affect mapping efficiency and accuracy. Therefore, this paper proposes a multi-robot collaborative mapping method based on point-line fusion to address this issue. This method is designed for indoor environments with weak-texture structures for localization and mapping. The feature-extraction algorithm, which combines point and line features, supplements the existing environment point feature-extraction method by introducing a line feature-extraction step. This integration ensures the accuracy of visual odometry estimation in scenes with pronounced weak-texture structure features. For relatively large indoor scenes, a scene-recognition-based map-fusion method is proposed in this paper to enhance mapping efficiency. This method relies on visual bag of words to determine overlapping areas in the scene, while also proposing a keyframe-extraction method based on photogrammetry to improve the algorithm’s robustness. By combining the Perspective-3-Point (P3P) algorithm and Bundle Adjustment (BA) algorithm, the relative pose-transformation relationships of multi-robots in overlapping scenes are resolved, and map fusion is performed based on these relative pose relationships. We evaluated our algorithm on public datasets and a mobile robot platform. The experimental results demonstrate that the proposed algorithm exhibits higher robustness and mapping accuracy. It shows significant effectiveness in handling mapping in scenarios with weak texture and structure, as well as in small-scale map fusion.