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Simultaneous Localization and Mapping (SLAM) is one of the key issues for mobile robots to achieve true autonomy. The implementations of SLAM could rely on a variety of sensors. Among many types of them, the laser-based SLAM approach is widely used owing to its high accuracy, even in poor lighting conditions. However, when in structure-less environments, laser modules will fail due to a lack of sufficient geometric features. Besides, motion estimation by moving lidar has the problem of distortion since range measurements are received continuously. To solve these problems, we propose a tightly-coupled SLAM integrating LiDAR and an integrated navigation system (INS) for unmanned vehicle navigation in campus environments. On the basis of feature extraction, a constraint equation for inter-frame point cloud features is constructed, and the pose solution results of the INS are added as a priori data for inter-frame point cloud registration. The Levenberg-Marquardt nonlinear least square method is used to solve the constraint equation to obtain inter-frame pose relationships. Map matching and loop closure detection methods are used to optimize the odometer, and the optimal pose information is obtained. The proposed SLAM algorithm is evaluated by comparing with the classic open-source laser SLAM algorithms on the campus dataset. Experimental results demonstrate that our proposed algorithm has certain advantages in estimating the trajectory error of the unmanned vehicle and has higher mapping performance.

Unmanned driving technology has enormous advantages in improving efficiency, saving costs and reducing safety hazards. In the current implementation of localization and navigation solutions in underground environments, there are problems of implementation difficulties, high costs, and time-consuming construction of maps. In order to solve the above problems, a localization and navigation method for underground mine autonomous driving based on local geometric topology map is proposed. A local geometric topology map has been designed. The main structure of the underground environment road network is represented by a topology map. The map defines roadways (sides) and intersections (nodes), and stores a local geometric map built around the node in each node to achieve precise positioning at the node. A localization method based on local geometric topology map is proposed, which uses a LiDAR-based intersection detection algorithm and intersection localization algorithm for global vehicle localization. A trajectory-following algorithm based on adaptive model predictive control (MPC) has been designed to ensure the path-tracking precision of vehicles turning at high curvature intersections. A simulation environment and vehicle simulation model for underground mines are constructed by using a 3D physical simulation platform. The simulation results show that this method can achieve underground mine autonomous driving localization and navigation functions. The positioning errors are within 0.2 m at various types of intersections, meeting the positioning localization precision requirements of autonomous driving. Throughout the entire driving process, the vehicle maintains a relatively stable driving state and a small tracking error. Compared with the current localization and navigation methods that rely on technologies such as 5G and UWB, this method only relies on two types of vehicle sensors: LiDAR and inertial measurement unit. It has great advantages in controlling equipment costs.

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Proliferation of deployed sea-going autonomous platforms, such as autonomous underwater vehicles (AUVs), unmanned surface vehicles (USV), and sensor nodes anchored to the seabed, make the deployment of true underwater acoustic networks more and more feasible. An important feature of any network is the ability to synchronize the clocks of the participants, for the purpose of, e.g., time-slotted media access control (MAC) and navigation. Terrestrial clock synchronization protocols, such as the well-established network time protocol (NTP), are not readily applicable to underwater acoustic networks, because of long propagation times, low packet delivery success rates, communication ranges that vary over time in an unpredictable manner, and, in the presence of mobile nodes, the ad hoc nature of the composition of the network. This paper proposes a continuous estimation of internode clock offset and drift, based on the continuous exchange of modem packets, possibly containing transmission and reception timestamps. The proposed solution takes explicitly into account the limitations of the acoustic communication channel and network node mobility. This robust, opportunistic clock synchronization (ROCS) is robust against modem reset, and will work even if packet delivery success rates are not optimal or if no communication is possible for extended periods of time. Experimental results are given from the COLLaborative Asw Behaviours–Next Generation Autonomous Systems (COLLAB-NGAS14) campaign, held October 19–31, 2014, off the west coast of Italy. During the sea trial, the proposed clock synchronization algorithm was deployed and successfully tested within an underwater acoustic network composed of mobile and fixed nodes. [ABSTRACT FROM PUBLISHER]

Mobile robot typically has limited on-board resources and may be applied in different indoor environment. Thus, it is necessary that they can learn a map and navigate themselves autonomously with lightweight algorithms. A novel topological map-building-based localization and navigation method is proposed in this paper. Based on the depth curve provided by a 3D sensor, a progressive Bayesian classifier is developed to realize direct corridor type identification. Instead of extracting features from single observation, information from multi-observations are fused to achieve a more robust performance. A topological map generation and loop closing method are proposed to build the environment map through autonomous exploration. Based on the derived map and the Markov localization method, the robot can then localize itself and navigate freely in the indoor environment. Experiments are performed on a recently built mobile robot system, and the results verify the effectiveness of the proposed methodology. [ABSTRACT FROM PUBLISHER]

In this paper, a spatial-temporal collaborative sequential Monte Carlo architecture for mobile robot localization is designed to well suites intelligent environment for service robotic system. A proposed algorithm, namely Distributed Proportional Allocation-Augmented Particle Filter (DPA-APF), resolves the sensor collaboration problem by the processes of augmented sampling, inter-node resampling, inner-node resampling and particle exchange. These procedures exploit data parallelism and pipelining of resampling operations and improve the scalability of distributed particle filters (PFs). Moreover, modified visual and laser sensor perception models are also addressed to guarantee reliable and accurate robot localization in dynamic scenarios that robot coexists with people. The proposed method is applied to a home-care robotic intelligent room with distributed smart nodes, and the experimental results validate the effectiveness of the proposed method, which is hopeful to reduce the gap that exists between PF theory and their implementation using networked hardware. [ABSTRACT FROM AUTHOR]

In this paper we describe the design, development, and experimental trials of a climbing robot for manufacturing and inspection applications within the aerospace industry. We describe the mechanical platform, which utilizes vacuum for attachment to vertical and overhanging surfaces, and a traction system that enables rapid movement of the robot over planar and curved surfaces of any orientation. The main applications considered during the research were the manufacturing processes for large external surface structures such as wings and the post-manufacturing and in-service inspection of such structures. The design of suitable tool packages for manufacturing and non-destructive testing is considered in the paper. The first tool package to be implemented and tested was a five-axis high precision drill, which is described. The control system is described within this paper along with the software architecture. The software architecture for the robot was generalized, allowing different robot configurations to be described and implemented rapidly through structured configuration files. Particular attention is paid to the robot's localization and navigation system, which provides tool point precision to aircraft manufacturing tolerances. The localization system uses data from several different types of sensors and combines these with information provided by a surface model to derive six-degrees-of freedom position and orientation using an extended Kalman filter to fuse the state information from the different sources. Tool point position is calculated through direct kinematic transformations. Improvements to this work are described, which utilize one of the initial releases of Lieca's new six-degrees-of-freedom precision measurement instruments, the Lieca LTD-800. We also summarize the experimental trials and the initial performance in terms of tool point precision and climbing performance. The concept of application of the robot and details of the technologies included in the robot that are outlined in this paper are the subject of separate pending patent applications filed by BAE SYSTEMS plc. [ABSTRACT FROM AUTHOR]