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In this paper, we propose a novel method to detect rectangular and triangular traffic signs in natural scenes according to the geometric features of the rectangle and triangle traffic signs. It is a Gradient-direction-based method that mainly includes four stages: gradient information classification, filter templates-based line segments with different orientation extraction, structural corners detection and rectangle/triangle detection. The extracted corners are categorized into different sets according to the orientation of the line segments, by which we can quickly extract rectangle and triangle traffic signs in image. And the experimental results demonstrate under different conditions are shown that the proposed method can detect rectangular and triangular traffic signs in natural scenes efficiently and robustness.

The naturalness of the panoramic image is an important criterion for evaluating the result of image stitching. When the overlapping region of the image has a low-texture area, the alignment is hard to accomplish due to incorrect and inadequate feature point matching pairs, and the salient structures are easily destroyed due to the unreasonable position of the optimal seam. To solve the problem, we use line segment matching information to assist the alignment of low-texture regions and use superpixel segmentation regions to filter the unreliable point correspondences. An improved seam-finding algorithm is finally used to blend images together. The experimental results demonstrate that the proposed method can get more natural stitching results.

Precise relative navigation between unmanned aerial vehicles (UAVs) in a GNSS-denied environment is a critical technology enabling collaborative missions. Range-based relative navigation allows other onboard optical sensors to be used for missions instead of for tracking other formation members. However, range-based relative navigation suffers from a well-known ambiguous solution problem. Approaches developed in previous studies include the use of one or more anchors, optical-flow measuring velocity, or multiple range sensors to solve the observability of the estimated states. In this study, a novel range-based collaborative relative navigation algorithm for multiple fixed-wing UAVs is proposed by integrated ranging-sensor with other commonly used onboard sensors such as low-cost IMUs, where anchor, optical-flow measurement or multi ranging sensor is not required. A relative motion estimation model is established in a floating horizontal frame; the sensor biases are augmented in the model to solve the colored noise problem. The observability of the estimated states is analyzed, and the observable criteria to guide the flight are obtained by introducing the Lie derivative criteria. The relative position vector must not be a constant in both direction and distance, and non-rectilinear trajectory is also required to obtain observability. A decentralized estimation strategy based on a consensus extended Kalman filter is designed for the system, with several physical constraints on the estimation in constructing the consensus to improve the observability. The proposed algorithm is tested and verified using standard Monte Carlo simulations. The simulation results indicate that the full state is observable if the observable criteria are satisfied. The sensitivity of the relative localization accuracy to the sensors and motion is presented and discussed. It is shown that the uncertainties from ranging-sensors and IMUs contribute more to the performance of the estimation. The estimated uncertainties of the sensor biases are also promising, and can potentially be used to improve the absolute navigation of UAVs.