Point cloud registration aims to align point clouds into a unified coordinate system, which serves as a foundational task for multivehicle collaborative mapping in outdoor environments. However, the significant perspective difference between aerial and ground vehicles results in remarkably low overlap between their point clouds, presenting a substantial challenge for accurate registration. To address this, we propose a real-time registration method with morphology-based representation for aerial-ground vehicles operating in diverse outdoor scenarios. In order to accurately model the ground surface, we perform a morphological operation to extract ground points, ensuring adaptability to diverse scenarios. For the purpose of identifying the overlapping region between aerial–ground point clouds, we stratify the point clouds upward from the ground and locate the regions with the most similar frequency distribution, overcoming the significant aerial–ground perspective difference. Toward real-time registration, we concentrate the graph-based maximal clique searching within the identified overlap regions, reducing the search space and computational cost. Extensive experiments on one self-recorded and one public aerial-ground point cloud datasets demonstrate that our proposed method achieves state-of-the-art registration accuracy while maintaining high computational efficiency.

This article proposes a perception-complementarity-driven trajectory generating method for multiple unmanned aerial vehicles (UAVs), which can effectively enhance the visibility of the target for UAVs in unknown environments. rgb0,0,0Traditional methods often rely on prior maps or additional sensors to assist with obstacle avoidance. Nevertheless, these methods are not only costly but also fail to effectively avoid occlusions caused by obstacles. rgb0,0,0Different from existing methods, the trajectory planned by the proposed method not only enables the vision-based UAVs to maintain the perception of obstacles and the target on the one hand, but also preserves topological equivalence with the predicted target trajectory on the other hand. Specifically, a vision-based mutual observation approach among UAVs is proposed to enhance the overall perception capability of the UAVs system. On this basis, a target-guided collaborative trajectory planning method is proposed to ensure the planned collision-free trajectory for other UAV in the formation maintains target visibility. In addition, a trajectory feasibility assessment method is proposed to obtain the collaborative trajectory planned by the UAV at the optimal observation location. Finally, comparative simulations are conducted with three state-of-the-art methods, demonstrating the advantages of the proposed method in maintaining target visibility and tracking efficiency during the vision-based aerial tracking. The real-world experiment demonstrates the feasibility of the proposed method.Note to Practitioners—Most existing vision-based multi-UAV target tracking methods require additional prior maps or laser sensors to assist in obstacle avoidance. In practical tracking scenarios, the limited perception range of cameras poses significant challenges for UAVs in synchronously observing moving target and environmental obstacles. The article proposes a trajectory generation method based on perceptual complementarity to ensure that the generated trajectory effectively perceives surrounding obstacles while enhancing the observation capability of UAVs towards moving target. The key insight of this work is to utilize mutual observation among multiple UAVs to assist in planning collision-free tracking trajectories, ensuring safety during the tracking process. Building upon this, by preserving topological equivalence with the predicted target trajectory, there is an improvement in the target visibility ratio during the tracking process. Furthermore, a trajectory feasibility assessment method is proposed to obtain the optimal collaborative trajectory from the UAV positioned at the optimal location in the formation. The effectiveness of the proposed method in improving flight safety and target visibility is validated through comparative experiments.

Cross-source place recognition is the foundation of collaborative mapping tasks between Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs) to achieve loop closure and global localization in outdoor environments. Due to the dynamic changes in communication bandwidth, the point density of point clouds transmitted from the robots to the server will also change simultaneously, which significantly affects the accuracy of place recognition. This article proposes a hierarchical BEV fusion network for unmanned aerial-ground vehicles with low-overlap and varying-density point clouds, called HBFusion. To improve the accuracy of place recognition for low-overlap point clouds, a multi-resolution voxel feature aggregation module based on sparse convolution is proposed to aggregate voxel features from multi-scale receptive fields to point features, which improves the similarity of global descriptors between low-overlap point clouds using rich point features. To adapt to point density variations caused by dynamic changes in communication bandwidth, we propose a hierarchical BEV fusion module to extract multi-layer BEV features at different heights, which improves the adaptability of global descriptors to point density variations using BEV representations. Extensive experiments performed on GrAco, a public aerial-ground dataset, and DUT-GA, our self-recorded aerial-ground dataset with low-overlap point clouds, indicate that our network achieves state-of-the-art performance in accuracy, even under the condition of varying point densities of point clouds.

This paper proposes a mapping uncertainty-aware point-wise Lidar Inertial Odometry (LIO), which synthesizes the point-wise point-to-plane match and map refreshment into a probabilistic model. As a result, it can address the issue of mismatching during point registration and remove in-frame motion distortion of Lidar sensors. Specifically, the uncertainty-aware map is designed to embody the uncertainty of map geometric features (points and planes), which comes from the Lidar point measurement and pose estimation. Then the map can be modeled in a probabilistic form. In addition, the proposed framework refreshes map at each Lidar point measurement to timely revise geometric features and provide non-delayed map. On the basis, the probabilistic point-to-plane match method is designed to seek a corresponding plane for each Lidar point in point registration, which can enhance the effectiveness of match and provide adaptive observation noises for more accurate state estimation. Comparative experiments on various public datasets are conducted to demonstrate the superior performance of the proposed framework in terms of higher accuracy and better robustness.

This article proposes a frontier-guided informative planner for unmanned aerial vehicle volumetric exploration and 3-D reconstruction, which can explore a complex unknown environment and provide the accurate truncated signed distance function reconstruction simultaneously. Different from the existing methods, the key insight of the proposed method is that the hybrid surface frontier is proposed to guide both the tree expansion of dynamic rapidly exploring random tree star and the informative trajectory generation. As a result, the proposed planner can achieve more efficient volumetric exploration with higher reconstruction quality. Specifically, hybrid global–local surface frontiers are designed to guide the potential viewpoints sampling and tree expansion, which results in directional exploration. Then, the hybrid surface frontiers are further leveraged to guide the candidate paths generation. On the basis, the path maximizing the new comprehensive gain is selected for the following B-spline trajectory optimization, which can further improve the reconstruction quality. Comparative simulation and real-world experiments are conducted to demonstrate the superior performance of the proposed method including the exploration efficiency and reconstruction quality.

Recent advances in trajectory planning have enabled quadrotor unmanned aerial vehicles (UAVs) to navigate autonomously in complex environments. However, most of the existing methods do not consider the perception quality and the safety simultaneously. This article proposes a perception-aware trajectory planning strategy for quadrotors, which can ensure the safety and localization accuracy. In contrast to the existing methods, the main idea of the proposed method lies in that the yaw angle trajectory is planned to actively obtain more information in the environment to improve the localization accuracy and keep the safe flight simultaneously. Following the mainstream two-stage motion planning framework, a coarse-to-fine graph search strategy is proposed to search for a safe and perception-aware yaw angle path in the first stage. Specifically, a Yaw Safety Corridor (YSC) is proposed to guarantee the safety, which can observe the obstacles directly along the tangent direction of the position trajectory. In addition, a dedicated map Fisher Information Field (FIF) is employed to evaluate the perception quality. In the second stage, a path-guided optimization method is proposed to quickly generate a safe and perception-aware trajectory. Finally, comparative simulation and real-world experiments are conducted to verify the superior performance in terms of the perception quality and the safety of the proposed method.

The perception-aware motion planning method based on the localization uncertainty has the potential to improve the localization accuracy for robot navigation. How-ever, most of the existing perception-aware methods pre-build a global feature map and can not generate the perception- aware trajectory in real time. This paper proposes a topology- guided perception-aware receding horizon trajectory generation method, which contains a topology-guided position trajectory generation and a perception-aware yaw angle trajectory generation. Specifically, a memorable active map is built by selectively storing the visual landmarks. After that, a library of candidate topological trajectories are generated, which are then evaluated in terms of the perception quality based on the active map, smoothness, collision possibility and feasibility. In addition, the yaw angle trajectory is obtained through a front-end multiple refined path search and a back-end path- guided trajectory optimization. Comparative simulation and real-world experiments are carried out to confirm that the proposed method can keep more visual features in view and reduce the localization error.

This article proposes a novel asymptotic robust control approach for rotorcraft unmanned aerial vehicles (UAVs), which can effectively eliminate the impact of external disturbances and the model uncertainties. Different from existing works, the proposed method alleviates the assumption that disturbances should have no variations in the existing observers for uncertainties. In addition, the equilibrium point of the entire observer-controller system is asymptotically stable without the assumption of the boundness of the outer-loop signals or the time-scale separation assumption. Specifically, two observer-based estimators are designed to estimate the model uncertainty and the external disturbance for the force and torque, respectively. On this basis, a nonlinear hierarchical tracking controller is then proposed with the feedforward compensated disturbance term. Despite the nonlinear coupled dynamics and the disturbances, a generic framework for the stability analysis is proposed to yield the asymptotic stability of the equilibrium point of the entire controller-observer system. Comparative experiments are conducted to show the superior performance of the proposed approach in terms of higher tracking accuracy and stronger robustness. Note to Practitioners—Most of the existing observer-based control approaches can only govern the rotorcraft closed-loop system to be ultimately uniformly bounded. The highly coupled dynamics and mismatched uncertainties in practice make the effective asymptotic robust control of rotorcrafts very challenging. A novel robust control approach for rotorcraft UAVs is proposed to yield the asymptotic stability of the equilibrium point despite the nonlinear coupled dynamics and the disturbances. The key insight of this work to guarantee the asymptotic stability of the system is that the nominal signals (i.e., the output of the nominal auxiliary dynamics) are fed back to the controller. In addition, the attitude error signal is proved to be exponentially convergent, which can further help prove the asymptotic stability of the entire controller-observer system. Comparative experiments are conducted to show the applicability of the proposed approach.

This article proposes a novel adaptive image-based output feedback visual servoing approach to control a multirotor to the desired pose by using a minimum onboard sensor suite, which consists of an inertial measurement unit and a monocular camera. Different from “perspective moment,” a new type of image feature is designed as “rotated perspective moment,” whose dynamics is independent of roll, pitch, and yaw rates. On this basis, a nonlinear adaptive observer is designed to estimate the scaled linear velocity, which is more accurate, since the observer does not involve noisy angular velocity measurements. Then, a novel image-based output feedback controller is proposed with the designed image features and the observer, wherein the new saturated integral terms of linear and angular velocity errors are introduced into the controller design, respectively, to compensate system uncertainties. As a result, the steady-state error is decreased considerably. In addition, without the assumption of the separation principle between the observer and the controller, the small-angle approximation, or the time-scale separation assumption, the error signals of image features, attitude, velocity, and observer estimation can all converge to the origin asymptotically, which is proven by rigorous Lyapunov analysis. Comparative experiments are conducted to show the superior performance of the proposed approach in terms of more accurate velocity estimation, smaller steady-state errors, and stronger robustness.