Abstract
To allow mobile robots to visually observe the temperature of equipment in complex industrial environments and work on temperature anomalies in time, it is necessary to accurately find the coordinates of temperature anomalies and obtain information on the surrounding obstacles. This paper proposes a visual saliency detection method for hypertemperature in three-dimensional space through dual-source images. The key novelty of this method is that it can achieve accurate salient object detection without relying on high-performance hardware equipment. First, the redundant point clouds are removed through adaptive sampling to reduce the computational memory. Second, the original images are merged with infrared images and the dense point clouds are surface-mapped to visually display the temperature of the reconstructed surface and use infrared imaging characteristics to detect the plane coordinates of temperature anomalies. Finally, transformation mapping is coordinated according to the pose relationship to obtain the spatial position. Experimental results show that this method not only displays the temperature of the device directly but also accurately obtains the spatial coordinates of the heat source without relying on a high-performance computing platform.
Highlights
In the path planning of mobile robots, it is common to construct a map using the dynamic vision fusion of cameras and multi-sensors [1,2,3]
The text uses the random sample consensus camera used in this article is a 200W pixel POE DS-2CD3T25-I3 with a focal length of four millimeters
The experimental results demonstrate that thethe method proposed in in this paper cancan fuse target
Summary
In the path planning of mobile robots, it is common to construct a map using the dynamic vision fusion of cameras and multi-sensors [1,2,3]. Used 3D visual reconstruction methods include feature extraction and matching, technology in real life has become extensive, attracting the attention of many experts and scholars [28,29]. Sparse point cloud reconstruction, camera pose solution, dense point cloud reconstruction, and Commonly used 3D visual reconstruction methods include feature extraction and matching, sparse surface reconstruction [30,31,32,33]. Through the research of different experts and scholars, related point cloud reconstruction, camera pose solution, dense point cloud reconstruction, and surface technologies such as feature matching, depth calculation, and mesh texture reconstruction have made reconstruction [30,31,32,33]. To reduce the calculation cost and dependence on training samples, this paper mainly uses characteristics of infrared images to detect the center coordinates of the heat source. Position of the heat source that needs to be operated using the above method
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