In this letter, we propose RI-LIO, a new reflectivity image assisted tightly-coupled LiDAR-inertial odometry (LIO) framework that introduces additional reflectivity texture information to efficiently reduce the drift of geometric-only methods. To achieve this, we construct an iterated extended Kalman filter framework by blending the point-to-plane geometric measurement and the reflectivity image measurement. Specifically, the geometric measurement is defined as the distance from the raw point of a new scan to its nearest neighbor plane in the global incremental kd-tree map. The searched nearest neighbor point is used to render a sparse reflectivity image after LiDAR motion distortion information is given by its corresponding raw point. Then, the reflectivity measurement is built to align the sparse reflectivity image with the dense reflectivity image of the current scan by minimizing the photometric errors directly. In addition, based on the mechanism of high-resolution LiDAR, a corrected spherical projection model is proposed to project spatial points into the image frame. Finally, extensive experiments are conducted in structured, unstructured and challenging open field scenarios. The results demonstrate that the proposed method outperforms existing geometric-only methods in terms of robustness and accuracy, especially in the rotation direction.

The unlawful use of noncooperative drones, or unmanned aerial vehicles (UAVs), poses serious threats to public safety and societal security, necessitating robust monitoring solutions. However, the detection of drones, particularly those flying remotely, is often hindered by the limited accuracy in identifying small targets. Additionally, challenges related to insufficient lightweight design and complexities in practical deployment further exacerbate the issue. To address these challenges, we propose a quad-head network, QHNet, designed to provide scalable, adaptable, and efficient drone detection across diverse scenarios. QHNet is available in five scalable model sizes—Nano (N), Small (S), Medium (M), Large (L), and Extra Large (X)—ensuring real-time detection tailored to varying resource constraints and operational requirements. The core of QHNet lies in its innovative quad detection head (QDH), which introduces an additional detection layer to perform secondary feature extraction for small targets, enhancing detection precision. Furthermore, the architecture integrates adaptive spatial feature fusion to improve scale invariance and overall detection accuracy. To further optimize performance, QHNet incorporates a four-scale feature fusion network (FSF) and a specialized small target IoU loss function (STIoU) to refine detection precision. Simultaneously, the lightweight coarse-to-fine processing unit (LCF) and SCDown downsampling (SCD) form a comprehensive lightweight scheme, significantly reducing computational overhead while maintaining high detection efficacy. Extensive experiments were conducted on DUT-Plus, an augmented version of the DUT Anti-UAV dataset enhanced through data augmentation. The experimental results confirm that QHNet attains outstanding performance, providing an advanced balance between detection accuracy and computational efficiency across all model sizes.

In recent years, various types of inspection robots have been developed to automate powerline inspection. The hybrid robot combines the advantages of climbing and flying robots and has a promising prospect in powerline inspection. But landing a hybrid robot on the target powerline among multiple ones is challenging. Flights require robust detection of powerlines and stable tracking of the target powerline. We propose a complete solution for autonomous landing of a hybrid robot on a powerline. First, a special feature extraction operator and the corresponding density-based feature recognition algorithm are designed to detect multi-scale powerlines. Second, a binocular vision-based depth estimation method for the landing point in the powerline is described. Third, two spatio-temporal dictionaries are established to track the target one in multiple powerlines. Meanwhile, landing strategies and control methods are presented to achieve a stable landing task. Finally, a hybrid robot is designed to validate the proposed method. The experiment results demonstrate the accuracy of the powerline detection and depth estimation algorithm, as well as the effectiveness of the robot in tracking and landing tasks.

In scenarios of the overhead power line system, manual methods are inefficient and unsafe. Meanwhile, the majority of cantilevered robots have poor efficiency when crossing obstacles. This letter proposes a novel power line inspection and maintenance robot to solve these problems. The robot employs a passive compliance obstacle-crossing principle, which could rapidly cross obstacles with the cooperation of gas springs and climbing wheels. Under high payload, the robot could take 5–15 seconds without any complex strategies to roll over obstacles. A variable configuration platform is also designed, which has a multiple line mode and a single line mode. It makes the robot suitable for different kinds of overhead power lines. Meanwhile, the related adaptability analyses are presented. Manipulators are also installed to help the robot perform specific maintenance tasks. The results of lab experiments and field tests reveal that the robot could stably and rapidly cross obstacles, such as suspension clamps, vibration dampers, and spacers, and could perform three kinds of maintenance tasks on the line.