Due to the insufficient accuracy, low frequency, discontinuity and deficiency in deck motion estimation, it is difficult for onboard monocular pose measurement to achieve robust autonomous landing guidance. To address the above issues, an on-board visual-inertial measurement method based on error state Kalman filter is put forward. The proposed framework tightly integrates 2D key points and IMU data to realize efficient and accurate relative pose and deck motion estimation under the constrained condition of dynamic backgrounds, moving target, et al. Considering the motion characteristics of the aircraft and ship, a novel asynchronous error state updating strategy is proposed to achieve high-precision performance. The experimental results demonstrate that the average relative positioning accuracy is improved by about 180% with average translation error decreasing to 3% of the counterpart compared to monocular methods. As to deck motion estimation, the average error of the ship Euler angle is about 0.1°. A cycle of state prediction and update can be conducted within 0.02 ms. The superior performance in accuracy and efficiency of relative and deck motion estimation guarantees significant capacity of the proposed method to integrate with various visual frontends, to perform sound autonomous landing guidance.

Monocular object pose tracking has been a key technology in autonomous rendezvous of two moving platforms. However, rapid relative motion between platforms causes large interframe pose shifts, which leads to pose tracking failure. Based on the derivation of the region-based pose tracking method and the theory of rigid body kinematics, we put forward that the stability of the color segmentation model and linearization in pose optimization are the key to region-based monocular object pose tracking. A reliable metric named VoI is designed to measure interframe pose shifts, based on which we argue that motion continuity recovery is a promising way to tackle the translation-dominant large pose shift issue. Then, a 2D tracking method is adopted to bridge the interframe motion continuity gap. For texture-rich objects, the motion continuity can be recovered through localized region-based pose transferring, which is performed by solving a PnP (Perspective-n-Point) problem within the tracked 2D bounding boxes of two adjacent frames. Moreover, for texture-less objects, a direct translation approach is introduced to estimate an intermediate pose of the frame. Finally, a region-based pose refinement is exploited to obtain the final tracked pose. Experimental results on synthetic and real image sequences indicate that the proposed method achieves superior performance to state-of-the-art methods in tracking objects with large pose shifts.

The autonomous landing guidance of fixed-wing aircraft in unknown structured scenes presents a substantial technological challenge, particularly regarding the effectiveness of solutions for monocular visual relative pose estimation. This study proposes a novel airborne monocular visual estimation method based on structured scene features to address this challenge. First, a multitask neural network model is established for segmentation, depth estimation, and slope estimation on monocular images. And a monocular image comprehensive three-dimensional information metric is designed, encompassing length, span, flatness, and slope information. Subsequently, structured edge features are leveraged to filter candidate landing regions adaptively. By leveraging the three-dimensional information metric, the optimal landing region is accurately and efficiently identified. Finally, sparse two-dimensional key point is used to parameterize the optimal landing region for the first time and a high-precision relative pose estimation is achieved. Additional measurement information is introduced to provide the autonomous landing guidance information between the aircraft and the optimal landing region. Experimental results obtained from both synthetic and real data demonstrate the effectiveness of the proposed method in monocular pose estimation for autonomous aircraft landing guidance in unknown structured scenes.

The autonomous landing guidance involves a large distance span, resulting in significant scale variations of the ship target in the image sequences obtained through monocular vision guidance. Existing pose measurement methods struggle to achieve high-precision monocular vision pose measurement across such a wide distance range. For current monocular vision pose measurement methods based on sparse keypoint sets, this paper focuses on improving the accuracy of keypoint detection, and analyzes the impact of target size and network input size on keypoint detection accuracy. Furthermore, this paper proposes a novel monocular vision pose measurement method based on multiple components, balancing both accuracy and efficiency. By using sparse keypoint sets to represent components in a simplified manner, and building on a coarse pose estimation of the overall ship target components, this method introduces a path aggregation feature pyramid network and a hierarchical encoding module to achieve high-precision detection of local component keypoints. Subsequently, by integrating the high-precision keypoint detection results of all components and solving the Perspective-n-Points (PnP) problem, the method achieves robust and high-precision pose measurement across the large distance span required for landing guidance. Simulation experiments and scaled physical experiments demonstrate that the proposed method achieves robust and high-precision monocular pose measurement across the large distance span for landing guidance, outperforming existing methods, with an average single-frame inference time of approximately 40 ms on embedded platforms.

Robust and accurate relative pose measurement is one of the key technologies for shipboard aircraft autonomous landing. The airborne monocular pose estimation methods, based on key-points detection and Perspective-n-Points (PnP) problem solving, have drawn significant attention of researchers for its strengths in ease of deployment, power efficiency and anti-electromagnetic interference, etc. However, the inaccuracy of key-points detection is not considered in the existing methods, which corrupts the precision of the pose identification. To address this problem, a tightly-coupled AEKF-based monocular pose tracking method is proposed. The pose measurement problem is transferred into motion state estimation of the aircraft. An extended Kalman filter system is established taking the key-point detection results as observations, with the carrier represented in form of a sparse key-point set. For the unknown statistics of the observations, an adaptive noise covariance estimation method based on sliding window is put forward. Synthetic and real scaled experiments demonstrate that the proposed method achieves robust and accurate online pose tracking, superior to traditional approaches.

Due to the portability and anti-interference ability, vision-based shipborne aircraft automatic landing systems have attracted the attention of researchers. In this paper, a Monocular Camera and Laser Range Finder (MC-LRF)-based pose measurement system is designed for shipborne aircraft automatic landing. First, the system represents the target ship using a set of sparse landmarks, and a two-stage model is adopted to detect landmarks on the target ship. The rough 6D pose is measured by solving a Perspective-n-Point problem. Then, once the rough pose is measured, a region-based pose refinement is used to continuously track the 6D pose in the subsequent image sequences. To address the low accuracy of monocular pose measurement in the depth direction, the designed system adopts a laser range finder to obtain an accurate range value. The measured rough pose is iteratively optimized using the accurate range measurement. Experimental results on synthetic and real images show that the system achieves robust and precise pose measurement of the target ship during automatic landing. The measurement means error is within 0.4° in rotation, and 0.2% in translation, meeting the requirements for automatic fixed-wing aircraft landing.Received 5 July 2022; revised 19 August 2022; accepted 27 September 2022.