Incremental Pose Map Optimization for Monocular Vision SLAM Based on Similarity Transformation.

The novel contribution of this paper is to propose an incremental pose map optimization for monocular vision simultaneous localization and mapping (SLAM) based on similarity transformation, which can effectively solve the scale drift problem of SLAM for monocular vision and eliminate the cumulative error by global optimization. With the method of mixed inverse depth estimation based on a probability graph, the problem of the uncertainty of depth estimation is effectively solved and the robustness of depth estimation is improved. Firstly, this paper proposes a method combining the sparse direct method based on histogram equalization and the feature point method for front-end processing, and the mixed inverse depth estimation method based on a probability graph is used to estimate the depth information. Then, a bag-of-words model based on the mean initialization K-means is proposed for closed-loop feature detection. Finally, the incremental pose map optimization method based on similarity transformation is proposed to process the back end to optimize the pose and depth information of the camera. When the closed loop is detected, global optimization is carried out to effectively eliminate the cumulative error of the system. In this paper, indoor and outdoor environmental experiments are carried out using open data sets, such as TUM and KITTI, which fully proves the effectiveness of this method. Closed-loop detection experiments using hand-held cameras verify the importance of closed-loop detection. This method can effectively solve the scale drift problem of monocular vision SLAM and has strong robustness.

Modeling and Flight Experiments for Swarms of High Dynamic UAVs: A Stochastic Configuration Control System with Multiplicative Noises.

UAV Swarm with high dynamic configuration at a large scale requires a high-precision mathematical model to fully exploit its boundary performance. In order to instruct the engineering application with high confidence, uncertainties induced from either systematic measurement or the environment cannot be ignored. This paper investigates the I t o ^ stochastic model of the UAV Swarm system with multiplicative noises. By combining the cooperative kinematic model with a simplified individual dynamic model of fixed-wing-aircraft for the first time, the configuration control model is derived. Considering the uncertainties in actual flight, multiplicative noises are introduced to complete the I t o ^ stochastic model. Following that, the estimator and controller are designed to control the formation. The mean-square uniform boundedness condition of the proposed stochastic system is presented for the closed-loop system. In the simulation, the stochastic robustness analysis and design (SRAD) method is used to optimize the properties of the formation. More importantly, the effectiveness of the proposed model is also verified using real data of five unmanned aircrafts collected in outfield formation flight experiments.

Formation Generation for Multiple Unmanned Vehicles Using Multi-Agent Hybrid Social Cognitive Optimization Based on the Internet of Things.

Multi-agent hybrid social cognitive optimization (MAHSCO) based on the Internet of Things (IoT) is suggested to solve the problem of the generation of formations of unmanned vehicles. Through the analysis of the unmanned vehicle formation problem, formation principles, formation scale, unmanned vehicle formation safety distance, and formation evaluation indicators are taken into consideration. The application of the IoT enables the optimization of distributed computing. To ensure the reliability of the formation algorithm, the convergence of MAHSCO has been proved. Finally, computer simulation and actual unmanned aerial vehicle (UAV) formation generation flight generating four typical formations are carried out. The result of the actual UAV formation generation flight is consistent with the simulation experiment, and the algorithm performs well. The MAHSCO algorithm based on the IoT is proved to be able to generate formations that meet the mission requirements quickly and accurately.

A Global Calibration Method for Widely Distributed Cameras Based on Vanishing Features.

This paper presents a global calibration method for widely distributed vision sensors in ring-topologies. Planar target with two mutually orthogonal groups of parallel lines is needed for each camera. Firstly, the relative pose of each camera and its corresponding target is found from the vanishing points and lines. Next, an auxiliary camera is used to find the relative poses between neighboring pairs of calibration targets. Then the relative pose from each target to the reference target is initialized by the chain of transformations, followed by nonlinear optimization based on the constraint of ring-topologies. Lastly, the relative poses between the cameras are found from the relative poses of calibration targets. Synthetic data, simulation images and real experiments all demonstrate that the proposed method is reliable and accurate. The accumulated error due to multiple coordinate transformations can be adjusted effectively by the proposed method. In real experiment, eight targets are located in an area about 1200 mm × 1200 mm. The accuracy of the proposed method is about 0.465 mm when the times of coordinate transformations reach a maximum. The proposed method is simple and can be applied to different camera configurations.