On-the-fly extrinsic calibration of multimodal sensing system for fast 3D thermographic scanning.

The fusion of three-dimensional (3D) geometrical and two-dimensional (2D) thermal information provides a promising method for characterizing temperature distribution of 3D objects, extending infrared imaging from 2D to 3D to support various thermal inspection applications. In this paper, we present an effective on-the-fly calibration approach for accurate alignment of depth and thermal data to facilitate dynamic and fast-speed 3D thermal scanning tasks. For each pair of depth and thermal frames, we estimate their relative pose by minimizing the objective function that measures the temperature consistency between a 2D infrared image and the reference 3D thermographic model. Our proposed frame-to-model mapping scheme can be seamlessly integrated into a generic 3D thermographic reconstruction framework. Through graphics-processing-unit-based acceleration, our method requires less than 10 ms to generate a pair of well-aligned depth and thermal images without hardware synchronization and improves the robustness of the system against significant camera motion.

Depth and thermal sensor fusion to enhance 3D thermographic reconstruction.

Three-dimensional geometrical models with incorporated surface temperature data provide important information for various applications such as medical imaging, energy auditing, and intelligent robots. In this paper we present a robust method for mobile and real-time 3D thermographic reconstruction through depth and thermal sensor fusion. A multimodal imaging device consisting of a thermal camera and a RGB-D sensor is calibrated geometrically and used for data capturing. Based on the underlying principle that temperature information remains robust against illumination and viewpoint changes, we present a Thermal-guided Iterative Closest Point (T-ICP) methodology to facilitate reliable 3D thermal scanning applications. The pose of sensing device is initially estimated using correspondences found through maximizing the thermal consistency between consecutive infrared images. The coarse pose estimate is further refined by finding the motion parameters that minimize a combined geometric and thermographic loss function. Experimental results demonstrate that complimentary information captured by multimodal sensors can be utilized to improve performance of 3D thermographic reconstruction. Through effective fusion of thermal and depth data, the proposed approach generates more accurate 3D thermal models using significantly less scanning data.