The ability of intelligent unmanned platforms to achieve autonomous navigation and positioning in a large-scale environment has become increasingly demanding, in which LIDAR-based Simultaneous Localization and Mapping (SLAM) is the mainstream of research schemes. However, the LIDAR-based SLAM system will degenerate and affect the localization and mapping effects in extreme environments with high dynamics or sparse features. In recent years, a large number of LIDAR-based multi-sensor fusion SLAM works have emerged in order to obtain a more stable and robust system. In this work, the development process of LIDAR-based multi-sensor fusion SLAM and the latest research work are highlighted. After summarizing the basic idea of SLAM and the necessity of multi-sensor fusion, this paper introduces the basic principles and recent work of multi-sensor fusion in detail from four aspects based on the types of fused sensors and data coupling methods. Meanwhile, we review some SLAM datasets and compare the performance of five open-source algorithms using the UrbanNav dataset. Finally, the development trend and popular research directions of SLAM based on 3D LIDAR multi-sensor fusion are discussed and summarized.

https://www.mdpi.com/2072-4292/14/12/2835/pdf?version=1655128131

A Review of Multi-Sensor Fusion SLAM Systems Based on 3D LIDAR

A private fifth generation (5G) network is a dedicated 5G network with enhanced communication characteristics, unified connectivity, optimized services, and customized security within a specific area. By subsuming the advantages of both public and non-public 5G networks, private 5G networks have found their applications across industry, business, utilities, and the public sector. As a promising accelerator for Industry 4.0, the concept of a private 5G network has recently attracted significant research attention from industry and academia. This article provides a comprehensive review of research on private 5G networks. Specifically, this paper first provides an overview of the concept and architecture of private 5G networks. It then discusses implementation issues and key enabling technologies for private 5G networks, followed by their more appealing use cases and existing real-life demonstrations. Finally, it examines some research challenges and future directions regarding private 5G networks.

Private 5G Networks: Concepts, Architectures, and Research Landscape

We present an efficient multi-sensor odometry system for mobile platforms that jointly optimizes visual, lidar, and inertial information within a single integrated factor graph. This runs in real-time at full framerate using fixed lag smoothing. To perform such tight integration, a new method to extract 3D line and planar primitives from lidar point clouds is presented. This approach overcomes the suboptimality of typical frame-to-frame tracking methods by treating the primitives as landmarks and tracking them over multiple scans. True integration of lidar features with standard visual features and IMU is made possible using a subtle passive synchronization of lidar and camera frames. The lightweight formulation of the 3D features allows for real-time execution on a single CPU. Our proposed system has been tested on a variety of platforms and scenarios, including underground exploration with a legged robot and outdoor scanning with a dynamically moving handheld device, for a total duration of 96 min and 2.4 km traveled distance. In these test sequences, using only one exteroceptive sensor leads to failure due to either underconstrained geometry (affecting lidar) or textureless areas caused by aggressive lighting changes (affecting vision). In these conditions, our factor graph naturally uses the best information available from each sensor modality without any hard switches.

Unified Multi-Modal Landmark Tracking for Tightly Coupled Lidar-Visual-Inertial Odometry

: The ability of intelligent unmanned platforms to achieve autonomous navigation and positioning in a large-scale environment has become increasingly demanding, in which LIDAR-based Simultaneous Localization and Mapping (SLAM) is the mainstream of research schemes. However, the LIDAR-based SLAM system will degenerate and affect the localization and mapping effects in extreme environments with high dynamics or sparse features. In recent years, a large number of LIDAR-based multi-sensor fusion SLAM works have emerged in order to obtain a more stable and robust system. In this work, the development process of LIDAR-based multi-sensor fusion SLAM and the latest research work are highlighted. After summarizing the basic idea of SLAM and the necessity of multi-sensor fusion, this paper introduces the basic principles and recent work of multi-sensor fusion in detail from four aspects based on the types of fused sensors and data coupling methods. Meanwhile, we review some SLAM datasets and compare the performance of ﬁve open-source algorithms using the UrbanNav dataset. Finally, the development trend and popular research directions of SLAM based on 3D LIDAR multi-sensor fusion are discussed and summarized.

remote sensing

As a sort of model-free tracking approach, discriminative correlation filter (DCF)-based trackers have shown prominent performance in unmanned aerial vehicle (UAV) tracking. Nevertheless, typical DCFs acquire all samples oriented to filter training merely from the current frame by cyclic shift operation in the spatial domain but ignore the consistency between samples across the timeline. The lack of temporal cues restricts the performance of DCFs under object appearance variations arising from object/UAV motion, scale variations, and viewpoint changes. Besides, many existing methods commonly neglect the channel discrepancy in object position estimation and generally treat all channels equally, thus limiting the further promotion of the tracking discriminability. This work proposes a novel tracking approach based on a multi-regularized correlation filter, i.e., MRCF tracker. By regularizing the deviation of responses and the reliability of channels, the tracker enables smooth response variations and adaptive channel weight distributions simultaneously, leading to favorable adaption to object appearance variations and enhancement of discriminability. Exhaustive experiments on five authoritative UAV-specific benchmarks validate the competitiveness and efficiency of MRCF against top-ranked trackers. Furthermore, we apply our proposed tracker to monocular UAV self-localization under air-ground robot coordination. Evaluations indicate the practicability of the presented method in UAV localization applications.

Multi-Regularized Correlation Filter for UAV Tracking and Self-Localization

Multiple mobile robots have gradually played a key role in many industrial systems, such as factory freight logistics system, patrol security in the factory environment, and multirobot collaborative service and work. As a key issue in industrial environment perception, the accurate robot localization can enhance their autonomous ability and is an important branch of robotic studies in artificial intelligence. In this paper, we propose a new method for cooperative autonomous localization among air-ground robots in a wide-ranging outdoor industrial environment. The aerial robot first maps an area of interest and achieves self-localization. Then the aerial robot transfers a simplified orthogonal perspective 2.5-D map to the ground robots for collaboration. Within the collaboration, the ground robot achieves pose estimation with respect to the unmanned aerial vehicle pose by instantaneously registering a single panorama with respect to the 2.5-D map. The 2.5-D map is used as the spatial association among air-ground robots. The ground robots estimate the orientation using automatically detected geometric information and generates the translation by aligning the 2.5-D map with a semantic segmentation of the panorama. Our method effectively overcomes the dramatic differences between the air-level view and the ground-level view. A set of experiments is performed in the outdoor industrial environment to demonstrate the applicability of our localization method. The proposed robotic collaborative localization outperforms most consumer sensor in accuracy and also has an outstanding running time.

Intelligent Collaborative Localization Among Air-Ground Robots for Industrial Environment Perception

A synchronous localization and mapping method for vision-inertia-laser fusion mainly relates to the technical fields of multi-sensor fusion, SLAM and the like. In order to solve a problem that single sensor SLAM has low precision and is easy to lose in localization and mapping, the invention provides a robust high precision vision, inertia, laser radar fusion SLAM system. A tightly-coupled visual inertia odometer is further optimized through laser radar scanning and matching so as to obtain a more accurate localization result. And when a camera or a laser radar degenerates, and a visual inertia module or a scanning and matching module can not work normally, a system automatically integrates remaining workable modules to maintain stable pose estimation. In order to remove cumulative errors, loopback detection based on an appearance and neighboring matching based on point cloud are added, and then six-degree-of-freedom pose optimization is performed to maintain global consistency. In the invention, robust high-precision localization and mapping effects can be obtained.

Synchronous localization and mapping method for vision-inertia-laser fusion

An asynchronous on-line calibration method for multi-sensor fusion is disclosed. The mutual conversion relationship between the coordinate systems of the three sensors are calibrated, and the measurement information of the plurality of sensors is unified into the same coordinate system, The traditional calibration method is limited to the calibration between two sensors, and requires strict time synchronization, so the calibration process is more complex. The invention calculates the motion of the laser radar, the camera and the inertial measurement unit (IMU) respectively, obtains the motionof different sensors in the same time interval by using linear interpolation, and finally obtains the external rotation between two sensors by aligning the rotation sequence of the three sensors. Theinvention can obtain more accurate external orientation between the lidar, the camera and the inertial measurement unit (IMU), and provides initial value for subsequent calibration.

An asynchronous on-line calibration method for multi-sensor fusion

The invention relates to a multi-line lidar and camera joint calibration method based on fine radar scanning edge points, which mainly relates to the technical fields of robot vision, multi-sensor fusion and the like. Because of the influence of the resolution of lidar, the scanned edge points are often not accurate enough, so the calibration results are not accurate enough. According to the characteristic that the range of the lidar point is abrupt at the edge, the invention searches and compares for many times, and takes the point closer to the edge as the standard point. By detecting the circle in the camera image and radar edge points, the translation between the camera and the lidar is calculated according to the pinhole camera model. The calibration parameter C is searched in the neighborhood space of the obtained translation vector to find the calibration result that minimizes the projection error. The invention can extract the points swept by the lidar on the edge of the objectwith high precision, avoids the problem of insufficient precision caused by low resolution of the lidar, and improves the calibration precision.

A multi-line lidar and camera joint calibration method based on fine radar scanning edge points

A loopback detection method based on point cloud segment matching constraint and trajectory drift optimization is proposed, which adopts real-time lidar odometer and LOAM system, but the LOAM system will have large drift after long-time mapping, so a method based on point cloud segment matching constraint and trajectory drift optimization is proposed. By segmenting, describing and matching the point cloud acquired by lidar, the loop relationship is found, and the loop is adjusted by using explicit loop closure ELCH algorithm after the loop is found, and an optimization algorithm is proposed toeliminate the local inconsistencies in the adjustment process of point cloud map. After optimization, the invention proposes a posture prediction and compensation algorithm to predict and compensatethe posture after the loop. So as to maximize the effect of reducing drift error.

A loop detection method based on point cloud segment matching constraint and trajectory drift optimization

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