Since GPS signals are unavailable for indoor navigation, current research mainly focuses on vision-based locating with a single mark. An obvious disadvantage with this approach is that locating will fail when the mark...Since GPS signals are unavailable for indoor navigation, current research mainly focuses on vision-based locating with a single mark. An obvious disadvantage with this approach is that locating will fail when the mark cannot be seen. The use of multiple marks can solve this problem. However, the extra process to design and identify different marks will significantly increase system complexity. In this paper, a novel vision-based locating method is proposed by using marks with feature points arranged in a radial shape. The feature points of the marks consist of inner points and outer points. The positions of the inner points are the same in all marks, while the positions of the outer points are different in different marks. Unlike traditional camera locating methods （the PnP methods）, the proposed method can calculate the camera location and the positions of the outer points simultaneously. Then the calculation results of the positions of the outer points are used to identify the mark. This method can make navigation with multiple marks more efficient. Simulations and real world experiments are carried out, and their results show that the proposed method is fast, accurate and robust to noise.展开更多
针对传统单图像的检测方法在内河航标检测中准确性与时效性差的问题,提出一种基于多图像特征融合的内河航标检测方法。在多图像快速鲁棒特征(speeded up robust feature,SURF)匹配搜索算法中加入一个确保特征样本饱和化的循环判断机制,...针对传统单图像的检测方法在内河航标检测中准确性与时效性差的问题,提出一种基于多图像特征融合的内河航标检测方法。在多图像快速鲁棒特征(speeded up robust feature,SURF)匹配搜索算法中加入一个确保特征样本饱和化的循环判断机制,防止关键特征缺失。采用曼哈顿距离完成图像特征的相似性度量,充分利用多图像间的融合信息提高航标清晰度。采用硬件感知网络架构与NetAdapt算法结合的MobileNetV3网络对YOLOv8的主干网络进行轻量化改进,降低模型空间复杂度。实验结果表明,对内河航标检测的平均精度均值(mean average precision,mAP)达到了97.02%,模型大小仅4.75 MB,推理耗时仅10.9 ms。改进方法能够快速、准确地检测定位到图像中的航标,能更好地适应特殊场景,为解决实际问题提供支持。展开更多
基金supported by National Basic Research Program of China (No.2010CB731800)
文摘Since GPS signals are unavailable for indoor navigation, current research mainly focuses on vision-based locating with a single mark. An obvious disadvantage with this approach is that locating will fail when the mark cannot be seen. The use of multiple marks can solve this problem. However, the extra process to design and identify different marks will significantly increase system complexity. In this paper, a novel vision-based locating method is proposed by using marks with feature points arranged in a radial shape. The feature points of the marks consist of inner points and outer points. The positions of the inner points are the same in all marks, while the positions of the outer points are different in different marks. Unlike traditional camera locating methods （the PnP methods）, the proposed method can calculate the camera location and the positions of the outer points simultaneously. Then the calculation results of the positions of the outer points are used to identify the mark. This method can make navigation with multiple marks more efficient. Simulations and real world experiments are carried out, and their results show that the proposed method is fast, accurate and robust to noise.
文摘针对传统单图像的检测方法在内河航标检测中准确性与时效性差的问题,提出一种基于多图像特征融合的内河航标检测方法。在多图像快速鲁棒特征(speeded up robust feature,SURF)匹配搜索算法中加入一个确保特征样本饱和化的循环判断机制,防止关键特征缺失。采用曼哈顿距离完成图像特征的相似性度量,充分利用多图像间的融合信息提高航标清晰度。采用硬件感知网络架构与NetAdapt算法结合的MobileNetV3网络对YOLOv8的主干网络进行轻量化改进,降低模型空间复杂度。实验结果表明,对内河航标检测的平均精度均值(mean average precision,mAP)达到了97.02%,模型大小仅4.75 MB,推理耗时仅10.9 ms。改进方法能够快速、准确地检测定位到图像中的航标,能更好地适应特殊场景,为解决实际问题提供支持。
