龚远瑾, 杨淑洁. 基于视觉语义和点线融合的无人艇动态水面环境感知SLAM算法[J]. 南方水产科学. DOI: 10.12131/20240019
引用本文: 龚远瑾, 杨淑洁. 基于视觉语义和点线融合的无人艇动态水面环境感知SLAM算法[J]. 南方水产科学. DOI: 10.12131/20240019
GONG Yuanjin, YANG Shujie. Dynamic water surface environment perception SLAM algorithm based on visual semantics and point-line fusion for unmanned surface vessels[J]. South China Fisheries Science. DOI: 10.12131/20240019
Citation: GONG Yuanjin, YANG Shujie. Dynamic water surface environment perception SLAM algorithm based on visual semantics and point-line fusion for unmanned surface vessels[J]. South China Fisheries Science. DOI: 10.12131/20240019

基于视觉语义和点线融合的无人艇动态水面环境感知SLAM算法

Dynamic water surface environment perception SLAM algorithm based on visual semantics and point-line fusion for unmanned surface vessels

  • 摘要: 无人艇在复杂水面环境下的自主导航能力是其完成养殖作业的基础,其在智能水产养殖中的发展前景巨大。视觉同时定位与地图构建 (Simultaneous location and mapping, SLAM) 技术可以提供实时的环境信息,是实现无人艇自主导航的关键。然而,水面环境是一种缺乏足够有效特征点的低纹理场景,且受水面波纹和反光影响存在大量动态无效特征点,导致视觉SLAM位姿的计算精度较差、性能严重下降。为此,提出了一种面向动态水面环境的基于视觉语义和点线融合的SLAM系统。首先,对ORB-SLAM3算法框架进行改进,增加语义分割线程,利用语义信息生成掩码消除水面无效特征点,以消除动态水面环境的干扰。其次,加入线特征来加强系统稳定性,提出了一种基于几何约束的线段匹配方法,提高水面线特征提取和跟踪的准确性,并利用点线特征融合提高数据关联的准确性,解决传统SLAM算法在水面低纹理场景中提取不足的问题。在USVInland数据集上的实验结果显示,与ORB-SLAM3和PL-SLAM算法相比,改进后算法的定位精度在直线航行中平均提高了44.74%和55.48%,在机动航行中最多提高了76.60%和70.15%,有效消除了水面干扰对位姿估计的影响,提升了视觉SLAM系统在水面低纹理场景中位姿估计的准确性和鲁棒性。

     

    Abstract: Unmanned surface vessels (USVs) have autonomous navigation capacities under complex water surface environment, which is the basis for aquaculture operation, with great development prospects in intelligent aquaculture. Simultaneous localization and mapping (SLAM) technology provides real-time environmental information, playing a pivotal role in enabling autonomous navigation for USVs. However, water surfaces present low-texture scenes with insufficient distinct features, compounded by dynamic invalid features caused by surface ripples and reflections, which result in poor calculation accuracy and serious performance degradation of visual SLAM pose. To address this problem, the paper proposes a SLAM system oriented towards dynamic water surface environments based on visual semantics and point-line fusion. Firstly, the ORB-SLAM3 framework is enhanced by introducing a semantic segmentation thread to eliminate invalid water features using semantic masks, thereby mitigating dynamic water interference. Secondly, the system stability is enhanced by incorporating line features to propose a geometric-constraint-based line-matching method to improve the accuracy of waterline feature extraction and tracking. Moreover, the point-line feature fusion is utilized to enhance data association accuracy, resolving the deficiency of traditional SLAM algorithms in extracting features from low-texture water surfaces. The results of the USVInland dataset demonstrate that compared with ORB-SLAM3 and PL-SLAM algorithms, the positioning accuracy of the improved algorithm has averagely increased by average of 44.74% and 55.48% in straight-line navigation, and up to 76.60% and 70.15% in maneuvering navigation, which mitigates the impact of water surface disturbances on pose estimation effectively, and enhances the accuracy and robustness of visual SLAM systems under low-texture water environment.

     

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