This article studies the leader-follower formation tracking problem of multiple quadrotors. In particular, the quadrotors are steered to track a reference trajectory while maintaining a desired configuration. The reference trajectory is partially available to a subset of quadrotors, and each quadrotor just interacts locally with its neighbors. A distributed control algorithm is proposed under a hierarchical framework. First, by introducing a distributed saturation control strategy, a command force is synthesized for the position to track the reference trajectory with a desired displacement. Second, a bounded applied force and a nonsingular command attitude are extracted from the synthesized command force based on a proper choice criterion of control parameters. Last but not the least, an applied torque is designed for the attitude to track the command one. In terms of Lyapunov theory, it is shown that the overall closed-loop error system is asymptotically stable. This indicates the accomplishment of the concerned leader-follower formation tracking control objective of multiple quadrotors. Finally, the effectiveness of the proposed distributed control algorithm is verified through both simulations and experiments.

Simultaneous localization and object tracking (SLOT) is essentially important for autonomous systems. Tightly-coupled and loosely-coupled methods are two commonly used back-end frameworks for the state-of-the-art solutions of SLOT problem. However, some inherent limitations exist in these two frameworks. In particular, the tightly-coupled method is usually disturbed by the poor observations of some dynamic objects, and the performance of a loosely-coupled one completely depends on that of classical static simultaneous localization and mapping (SLAM) process. Motivated by these observations, we propose a novel switching-coupled back-end solution and theoretically derive its concrete form using probability representation. Based on the switching strategy and the proposed objects classification criteria where the object uncertainty, observation quality and prior information are jointly considered, the dynamic objects' states are flexibly coupled with camera's state and static landmarks' states. For implementation, the measurement constraints of “good” dynamic objects and static landmarks are simultaneously leveraged to perform SLAM and good object tracking (SLAMGOT) process, and those of “bad” objects are used for bad object tracking (BOT) process based on the obtained camera state. Extensive evaluations on synthetic scenes, KITTI datasets and real-world experiments demonstrate the performance of the proposed method.

We study synchronization of coupled linear systems over networks with weak connectivity and nonuniform timevarying delays. We focus on the case where the internal dynamics are time-varying but non-expansive (stable dynamics with a quadratic Lyapunov function). Both uniformly jointly connected and infinitely jointly connected communication topologies are considered. A new concept of S-synchronization is introduced. We first show that global asymptotic S-synchronization can be achieved over directed networks with uniform joint connectivity and arbitrarily bounded delays. We then study the case of infinitely jointly connected communication topology. In particular, for the undirected communication topologies, it turns out that the existence of a uniform time interval for the jointly connected communication topology is not necessary and S-synchronization can be achieved when the time-varying nonuniform delays are arbitrarily bounded. Simulation results are provided to validate the theoretical results.

The applicability of SLAM algorithms is largely limited to the assumption of scene rigidity. Moreover, dynamic object perception is an essential technique for many robotic applications such as autonomous driving and multi-robot collaboration. In this letter, we present PointSLOT, an online, real-time and general stereo system that simultaneously performs SLAM and dynamic object tracking without any artificial priors. Unlike previous SLOT systems, we explicitly identify moving and static objects by computing the object motion probabilities, such that features from static objects are utilized to improve the camera pose estimation instead of treating all object regions as outliers. Based on the camera-centric parameterization for the object pose, the trajectories and dynamic landmarks of multi-keyframe objects are efficiently computed through an object-based bundle adjustment approach. The evaluations on KITTI datasets and real-world experiments show that the proposed system achieves comparable results to state-of-the-art solutions on both camera motion estimation and dynamic object tracking. The open-source code is available at https://github.com/pkzhou/PointSLOT .

In order to improve the accuracy of simultaneous localization and mapping problem, plane motion assumption is often used for advanced ground vehicle SLAM system. However, such an assumption is not always suitable to complex and changeable road scenes. In this letter, we propose a stereo-vision based SLAM framework that tightly couples the local ground manifold constraints into accurate camera trajectory estimation. Instead of considering a planar manifold assumption, we model the road as a sequence of local planes with different slopes named local ground manifolds (LGM). The impact region of the LGM is represented as a spherical area in the map, where the vehicle’s motion is constrained by the corresponding local plane model. ORB features and road segmentations are utilized to perform the environmental reconstruction and ground manifold representation. The structures of surroundings and the plane normal of LGMs are jointly optimized with the trajectory of the vehicle within a novel point-LGM tightly-coupled bundle adjustment framework. The experiments on KITTI datasets demonstrate that the proposed ground manifold representation can greatly benefit the camera trajectory estimation.

In Global Navigation Satellite System (GNSS)-denied environments, image registration has emerged as a prominent approach to utilize visual information for estimating the position of Unmanned Aerial Vehicles (UAVs). However, traditional image-registration-based localization methods encounter limitations, such as strong dependence on the prior initial position information. In this paper, we propose a systematic method for UAV geo-localization. In particular, an efficient range–visual–inertial odometry (RVIO) is proposed to provide local tracking, which utilizes measurements from a 1D Laser Range Finder (LRF) to suppress scale drift in the odometry. To overcome the differences in seasons, lighting conditions, and other factors between satellite and UAV images, we propose an image-registration-based geo-localization method in a coarse-to-fine manner that utilizes the powerful representation ability of Convolutional Neural Networks (CNNs). Furthermore, to ensure the accuracy of global optimization, we propose an adaptive weight assignment method based on the evaluation of the quality of image-registration-based localization. The proposed method is extensively evaluated in both synthetic and real-world environments. The results demonstrate that the proposed method achieves global drift-free estimation, enabling UAVs to accurately localize themselves in GNSS-denied environments.