The reconstruction of flame from the captured images is a difficult and computationally expensive problem. Reconstruction from color images will keep the colorful appearance, as is beneficial for visually realistic flame modeling. Most of existing color-image-based methods rebuild three density fields from RGB intensities; however, these methods suffer from the color distortion problem due to the high correlation of RGB intensities. A novel method for 3D flame reconstruction using color temperature is presented in this paper. Color-temperature mapping is calculated to avoid color distortion; this method maps the RGB intensities into the color temperature and its joint intensity. We improve the multiplication reconstruction with visual hull restriction so that the energy distribution is more reasonable, which allows avoidance of the impossible zones. Experimental results indicate that our approach is efficient in the visually plausible 3D flame generation and produces better color restorations.

Two-color pyrometric methods have been widely used in noncontact temperature measurement area. However, it is difficult to get synchronous monochromatic images for twocolor pyrometric formula. Some researches use beam splitter to obtain two or more optical paths to capture the different monochromatic images, but the complex optical paths will bring spatiotemporal matching errors. Another method uses color camera to capture the Red, Green, Blue (RGB) channel images as the RGB monochromatic images, but substituting the Dirac delta function for spectral response function will result in the inaccuracy of the measurement results. In fact, the RGB monochromatic images can be obtained from the color image if the irradiance attenuations from color channel to single wavelength are calibrated. In this paper, a novel 3-D reconstruction method is proposed to measure the temperature distribution of combustion flame. First, the irradiance attenuations are calibrated to calculate the synchronous monochromatic images at R and G wavelengths. Second, the tomographic reconstruction of flame monochromatic emissive power is improved with visual hull restriction so that the energy distribution is more reasonable. Finally, the 3-D temperature distribution is calculated from the reconstructed monochromatic emissive power fields at R and G wavelengths using two-color pyrometric method. The alcohol and butane flames are tested in the laboratory-scale test rig. The experimental results indicate that our approach performs well in flame temperature field reconstruction.Index Terms-3-D reconstruction, flame temperature distribution, monochromatic irradiance, temperature field measurement, two-color pyrometry.

This paper designs a motion rule suitable for grid environment and proposes a multi-robot autonomous obstacle avoidance method based on deep reinforcement learning. The training and validation of the method are done under the Stage simulation platform based on ROS operating system. During the training process, the robot uses Lidar to obtain the surrounding state information and generates actions based on the state information to obtain rewards, and the robot is guided by the rewards to optimize the strategy. Based on the D3QN algorithm, a new reward function is designed, a proximity penalty is introduced to reduce the collision between robots, a distance reward is added to guide the robot to complete the task, a step reward is added to improve the efficiency of the robot to complete the task, and an illegal action penalty is added to avoid the robot to choose an illegal action; the input is 5 frames of Lidar data, and in the network structure, the agent can better learn the correlation between the data by introducing Long Short Term Memory(LSTM) layer, and introducing Convolutional Block Attention Module(CBAM), a hybrid attention mechanism to allow the robot to pay more attention to the information of the surrounding robots. By designing experiments, we demonstrate that the learned strategy can effectively guide the robot through obstacle avoidance and complete the task.