Dynamic 3-D Reconstruction of Human Targets via an Omni-Directional Thermal Sensor

We explore the dynamic 3-D reconstruction (D3DR) of the target view in real-time images from the omni-directional (O-D) thermal sensor for intelligent perception of robotic systems. Recent O-D 3-D reconstruction methodologies are mainly focused on O-D visible-band vision for localization, mapping, calibration, and tracking, but there is no significant research for thermal O-D. The 3-D reconstruction from O-D images and the use of O-D thermal vision have not been sufficiently addressed. The thermal O-D images do not provide sharp-edge boundaries as in color vision cameras due to texture and mirror distortion. In order to fully address O-D thermal 3-D reconstruction, we proposed the D3DR method that dynamically detects the target region and densely reconstructs the detected target region to solve the non-sharp-edge boundaries' issue. We analyzed several different imaging positions, different baseline distances, and target distances with respect to the robot position for the best coverage of the target view with a minimum reconstruction error. We also look at the optimum number of observations for reconstruction using an optimization to find the compromise between accuracy, methodology, and number of observations. The benefits of this method are the accurate distance of the target from the camera, high accuracy, and low computation time of 3-D reconstruction.