Visible images have been widely used for motion estimation. Thermal images, in contrast, are more challenging to be used in motion estimation since they typically have lower resolution, less texture, and more noise. In this paper, a novel dataset for evaluating the performance of multi-spectral motion estimation systems is presented. All the sequences are recorded from a handheld multi-spectral device. It consists of a standard visible-light camera, a long-wave infrared camera, an RGB-D camera, and an inertial measurement unit (IMU). The multi-spectral images, including both color and thermal images in full sensor resolution (640 × 480), are obtained from a standard and a long-wave infrared camera at 32Hz with hardware-synchronization. The depth images are captured by a Microsoft Kinect2 and can have benefits for learning cross-modalities stereo matching. For trajectory evaluation, accurate ground-truth camera poses obtained from a motion capture system are provided. In addition to the sequences with bright illumination, the dataset also contains dim, varying, and complex illumination scenes. The full dataset, including raw data and calibration data with detailed data format specifications, is publicly available.

Multi-spectral sensors consisting of a standard (visible-light) camera and a long-wave infrared camera can simultaneously provide both visible and thermal images. Since thermal images are independent from environmental illumination, they can help to overcome certain limitations of standard cameras under complicated illumination conditions. However, due to the difference in the information source of the two types of cameras, their images usually share very low texture similarity. Hence, traditional texture-based feature matching methods cannot be directly applied to obtain stereo correspondences. To tackle this problem, a multi-spectral visual odometry method without explicit stereo matching is proposed in this paper. Bundle adjustment of multi-view stereo is performed on the visible and the thermal images using direct image alignment. Scale drift can be avoided by additional temporal observations of map points with the fixed-baseline stereo. Experimental results indicate that the proposed method can provide accurate visual odometry results with recovered metric scale. Moreover, the proposed method can also provide a metric 3D reconstruction in semi-dense density with multi-spectral information, which is not available from existing multi-spectral methods.

To deal with the increasing penetration of uncertainties caused by renewable generations and uncertain loads, this paper proposes a novel robust optimization model for the optimal power flow (OPF) problem of power systems. In the model proposed, the generation and the network topology are co-optimized, and then the proposed robust optimal power flow with transmission switching (ROPF_TS) model is converted to be a two-stage mixed-integer optimization model based on the duality theory. Based on Benders decomposition, the model is decomposed into a master problem and a slave problem, the master problem corresponds to the resolution of the deterministic optimal power flow with transmission switching problem under base-point mode, the slave problem corresponds to the robust optimal power flow with the given network topology, and then the feasible domain of the master problem is restrained by the Benders cuts developed by the slave problem. Finally, Example analyses are conducted to verify the validity of the proposed model and its solution methodology.

This paper proposes a novel optimization model for optimal reactive power flow concerning wind power generation, which considers generator voltage regulation characteristics. Synchronous generators (SGs) static excitation error control characteristics and doubly fed induction generator(DFIG) wind farm reactive power (var)-voltage control characteristics are considered. An extra inner potential node is introduced to reflect the excitation limits. Complementarity constraints are applied to represent the wind farm var-voltage control. Power fluctuation caused by the variability and uncertainty of wind generation are considered based on scenario analysis. Nonlinear primal-dual interior point algorithm is used to get the optimal strategies. Case study demonstrates effectiveness of the proposed approach.