Design of a Multi-Source Heterogeneous Data-Driven Intelligent Loading Decision System for Tobacco Logistics

Accurate localization and reliable mapping is essential for autonomous navigation of robots. As one of the core technologies for autonomous navigation, Simultaneous Localization and Mapping (SLAM) has attracted widespread attention in recent decades. Based on vision or LiDAR sensors, great efforts have been devoted to achieving real-time SLAM that can support a robot’s state estimation. However, most of the mature SLAM methods generally work under the assumption that the environment is static, while in dynamic environments they will yield degenerate performance or even fail. In this paper, first we quantitatively evaluate the performance of the state-of-the-art LiDAR-based SLAMs taking into account different pattens of moving objects in the environment. Through semi-physical simulation, we observed that the shape, size, and distribution of moving objects all can impact the performance of SLAM significantly, and obtained instructive investigation results by quantitative comparison between LOAM and LeGO-LOAM. Secondly, based on the above investigation, a novel approach named EMO to eliminating the moving objects for SLAM fusing LiDAR and mmW-radar is proposed, towards improving the accuracy and robustness of state estimation. The method fully uses the advantages of different characteristics of two sensors to realize the fusion of sensor information with two different resolutions. The moving objects can be efficiently detected based on Doppler effect by radar, accurately segmented and localized by LiDAR, then filtered out from the point clouds through data association and accurate synchronized in time and space. Finally, the point clouds representing the static environment are used as the input of SLAM. The proposed approach is evaluated through experiments using both semi-physical simulation and real-world datasets. The results demonstrate the effectiveness of the method at improving SLAM performance in accuracy (decrease by 30% at least in absolute position error) and robustness in dynamic environments.

The purpose of this research is to develop an AGV (Automatic Guided Vehicle) which has high accurate position control and mapping using a laser rangefinder. Recently, some factories are introducing AGVs in order to respond flexibly to a variety of conveyance request. Generally, a method of driving an AGV is using markers, wires or magnets. We manufactured an AGV which do self-location estimation by using a laser rangefinder without markers, wires or magnets. The AGV consists of a servo driver, AC servo motors, two batteries and a laser rangefinder. On this study, we have created a map of the experimental environment with SLAM (Simultaneous Localization and Mapping). The AGV performed the self-position estimation using a created map and it could move autonomously from a start position to an end position that we set in advance. We aim to improve the accuracy the AGV move by Spline interpolation.

In the absence of GPS signals, Simultaneous Localization and Mapping (SLAM) technology enables unmanned systems to accomplish map construction and autonomous navigation in unknown environments. At present, there are mainly methods for SLAM based on sensors such as lidar, camera, ultrasonic, Inertial Measurement Unit (IMU) and odometer for location environment. However, there are certain limitations in using a single sensor for SLAM in unknown environment. In order to solve the above problems, this paper summarizes the key problems faced by mainstream SLAM at present, and proposes a SLAM method based on multi-sensor fusion, which can make full use of the advantages of various sensors from the hardware structure and make up for the shortcomings of using a single sensor. Finally, the proposed method was verified on the platform of “JAC Electric Vehicle”, and the experimental results show that the proposed method can effectively avoid the influence caused by signal weakening, improve the positioning accuracy, and the system has stronger robustness and better tracking performance.

The millimeter-wave (mmWave) communication technology, which employs large-scale antenna arrays, enables inherent sensing capabilities. Simultaneous localization and mapping (SLAM) can utilize channel multipath angle estimates to realize integrated sensing and communication design in 6G communication systems. However, existing works have ignored the significant overhead required by the mmWave beam management when implementing SLAM with angle estimates. This study proposes a joint beam management and SLAM design that utilizes the strong coupling between the radio map and channel multipath for simultaneous beam management, localization, and mapping. In this approach, we first propose a hierarchical sweeping and sensing service design. The path angles are estimated in the hierarchical sweeping, enabling angle-based SLAM with the aid of an inertial measurement unit (IMU) to realize sensing service. Then, feature-aided tracking is proposed that utilizes prior angle information generated from the radio map and IMU. Finally, a switching module is introduced to enable flexible switching between hierarchical sweeping and feature-aided tracking. Simulations show that the proposed joint design can achieve sub-meter level localization and mapping accuracy (with an error < 0.5 m). Moreover, the beam management overhead can be reduced by approximately 40% in different wireless environments.

The most fundamental requirements for achieving accurate navigation of mobile robots are localization, map building and path planning. Currently, the mainstream methods include magnetic peg navigation, laser navigation, and 2D code navigation, etc. SLAM technology has the advantages of automation, intelligence, environmental adaptability, and low cost, and AGVs using this algorithm have been widely used in indoor applications and logistics transportation. Therefore, the SLAM (Simultaneous Localization and Mapping) algorithm based Automated Guided Vehicle (AGV) has become a hot research topic at home and abroad. In this paper, we investigate the SLAM algorithm in AGV system, and build indoor and logistics simulation environment maps based on 2D laser SLAM technology of Hector, Cartographer, Gmapping and Karto algorithms, and compare them by building accuracy index. The results show that the Hector algorithm has faster map building speed and accuracy, and is more suitable for indoor and logistics AGV systems.

The developing wireless charging technology has great potential for application on Automated Guided Vehicles (AGV). However, due to the strict requirements for high-efficiency wireless charging, it is difficult to navigate towards the charging pile at one go, especially under large-scale semi-structured or unstructured scenes where there have not enough features to obtain precise enough global positioning. Furthermore, single-stage planning relies largely on AGV's control performance, which often leads to failed alignment. In this paper, we discuss the factors that impact wireless charging efficiency and determine the required positioning accuracy. Accordingly, a multi-stage perception, positioning and planning method composed of coarse positioning, dynamic adjustment, and end-to-end alignment is proposed. In the coarse positioning stage, the use of iterative closest point (ICP) and simultaneous localization and mapping (SLAM) allows the AGV to relocate and reach the designated area flexibly and quickly. In the dynamic adjustment stage, AprilTag and LiDAR inertial odometry (LIO) is fused to estimate AGV's pose more accurately by addressing problems with AprilTag exceeding camera's field of view (FOV) or large observation errors at long distance. Finally, high-precision positioning information is obtained in the end-to-end alignment stage with coupled monocular camera and 2D LiDAR measurements. In each stage, different path constraint and obstacle avoidance strategy for motion planning are adopted to achieve higher efficiency. We show that with the proposed method, automatic wireless charging tasks could be performed without the need of readjustment, even under unstructured environment.

Simultaneous Localization And Mapping (SLAM) has become a crucial aspect in the fields of autonomous driving and robotics. One crucial component of visual SLAM is the Field-of-View (FoV) of the camera, as a larger FoV allows for a wider range of surrounding elements and features to be perceived. However, when the FoV of the camera reaches the negative half-plane, traditional methods for representing image feature points using <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$[u,v,1]^T$</tex-math> </inline-formula> become ineffective. While the panoramic FoV is advantageous for loop closure, its benefits are not easily realized under large-attitude-angle differences where loop-closure frames cannot be easily matched by existing methods. As loop closure on wide-FoV panoramic data further comes with a large number of outliers, traditional outlier rejection methods are not directly applicable. To address these issues, we propose LF-VISLAM, a <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V</b> isual <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I</b> nertial <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SLAM</b> framework for cameras with extremely <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</b> arge <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</b> oV with loop closure. A three-dimensional vector with unit length is introduced to effectively represent feature points even on the negative half-plane. The attitude information of the SLAM system is leveraged to guide the feature point detection of the loop closure. Additionally, a new outlier rejection method based on the unit length representation is integrated into the loop closure module. We collect the PALVIO dataset using a <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</b> anoramic <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</b> nnular <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</b> ens (PAL) system with an entire FoV of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$360^\circ{\times}(40^\circ{\sim}120^\circ)$</tex-math> </inline-formula> and an Inertial Measurement Unit (IMU) for <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V</b> isual <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I</b> nertial <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</b> dometry (VIO) to address the lack of panoramic SLAM datasets. Experiments on the established PALVIO and public datasets show that the proposed LF-VISLAM outperforms state-of-the-art SLAM methods. Our code will be open-sourced at https://github.com/flysoaryun/LF-VISLAM. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Motivated by the challenges of handling large-FoV cameras in SLAM applications, this paper proposes LF-VISLAM, a novel SLAM framework that uses a large-FoV camera and IMU sensors. Our framework is equipped with a loop closure thread that can use attitude information to eliminate accumulated errors. We have made algorithmic adjustments and optimizations to the negative half-plane features to better adapt to cameras with large FoV. Experimental evaluations demonstrate that LF-VISLAM significantly outperforms traditional SLAM methods. Additionally, the code will be open-sourced, providing easy access for research and implementation. Overall, LF-VISLAM is a promising solution to improve the performance of SLAM in challenging environments of autonomous driving and robotics.

Automated Guided Vehicles (AGV) with magnetic navigation and electromagnetic navigation have lower path flexibility due to the need to lay the path ahead of time. The Simultaneous Localization and Mapping (SLAM) is mature in technology theory, so that this technique is applied to the AGV navigation. The laser sensor is used to acquire the information in the location environment for immediate location and mapping, so that carry out the path planning and complete the AGV autonomous navigation. Extended Kalman Filter (EKF) is the most common solution for laser SLAM. Firstly, this paper analyzes the research results of EKF-SLAM at this stage and its limitations. Secondly, it discusses the application of EKF-SLAM in AGV and the development direction in the next stage. By comparative analysis shows that laser EKF-SLAM can meet the requirements of AGV navigation. The development of EKF-SLAM algorithm in positioning, mapping accuracy and the ability to solve interferences in dynamic environment has an important reference for the reliability of AGV navigation.

In recent years, the research of autonomous driving and mobile robot technology is a hot research direction. The ability of simultaneous positioning and mapping is an important prerequisite for unmanned systems. Lidar is widely used as the main sensor in SLAM (Simultaneous Localization and Mapping) technology because of its high precision and all-weather operation. The combination of Lidar and IMU (Inertial Measurement Unit) is an effective method to improve overall accuracy. In this paper, multi-line Lidar is used as the main data acquisition sensor, and the data provided by IMU is integrated to study robot positioning and environment modeling. On the one hand, this paper proposes an optimization method of tight coupling of lidar and IMU using factor mapping to optimize the mapping effect. Use the sliding window to limit the number of frames optimized in the factor graph. The edge method is used to ensure that the optimization accuracy is not reduced. The results show that the point plane matching mapping method based on factor graph optimization has a better mapping effect and smaller error. After using sliding window optimization, the speed is improved, which is an important basis for the realization of unmanned systems. On the other hand, on the basis of improving the method of optimizing the mapping using factor mapping, the scanning context loopback detection method is integrated to improve the mapping accuracy. Experiments show that the mapping accuracy is improved and the matching speed between two frames is reduced under loopback mapping. However, it does not affect real-time positioning and mapping, and can meet the requirements of real-time positioning and mapping in practical applications.

This article presents a new sensor fusion method for visual simultaneous localization and mapping (SLAM) through integration of a monocular camera and a 1D-laser range finder. Such as a fusion method provides the scale estimation and drift correction and it is not limited by volume, e.g., the stereo camera is constrained by the baseline and overcomes the limited depth range problem associated with SLAM for RGBD cameras. We first present the analytical feasibility for estimating the absolute scale through the fusion of 1D distance information and image information. Next, the analytical derivation of the laser-vision fusion is described in detail based on the local dense reconstruction of the image sequences. We also correct the scale drift of the monocular SLAM using the laser distance information which is independent of the drift error. Finally, application of this approach to both indoor and outdoor scenes is verified by the Technical University of Munich dataset of RGBD and self-collected data. We compare the effects of the scale estimation and drift correction of the proposed method with the SLAM for a monocular camera and a RGBD camera.

Visual Simultaneous Localization and Mapping (SLAM) algorithms relying on cameras have gained significant attention with advancements in computer vision. However, existing approaches often depend on high-performance hardware, limiting their applicability in resource-constrained scenarios. This study addresses indoor planar motion applications, such as service robots and automated guided vehicles (AGV), by proposing a cost-effective Visual-Inertial SLAM(VI-SLAM) framework tailored for low-performance platforms using a low-cost binocular camera and an inertial measurement unit (IMU). A localization module is developed to enhance feature stability through inertial prediction and reprojection cross validation, using grayscale images, depth images, and IMU data. A map correction module is introduced to integrate backend optimized poses, loop closure detection, and depth information, enabling the generation of globally consistent probability grid maps via submap stitching and multi-state depth image conversion. Comprehensive evaluations are conducted on public datasets and real-world environments. The results demonstrate the accuracy, robustness, and feasibility of our approach for low-performance and low-cost devices, highlighting its potential for commercialization and practical applications.

Simultaneous localization and mapping (SLAM) represents a crucial algorithm in the autonomous navigation of ground vehicles. Several studies were conducted to improve the SLAM algorithm using various sensors and robot platforms. However, only a few works have focused on applications inside low-illuminated featureless tunnel environments. In this work, we present an improved SLAM algorithm using wheel encoder data from an autonomous ground vehicle (AGV) to obtain robust performance in a featureless tunnel environment. The improved SLAM system uses FAST-LIO2 LiDAR SLAM as the baseline algorithm, and the additional wheel encoder sensor data are integrated into the baseline SLAM structure using the extended Kalman filter (EKF) algorithm. The EKF algorithm is used after the LiDAR odometry estimation and before the mapping process of FAST-LIO2. The prediction step uses the wheel encoder and inertial measurement unit (IMU) data, while the correction step uses the FAST-LIO2 LiDAR state estimation. We used an AGV to conduct experiments in flat and inclined terrain sections in a tunnel environment. The results showed that the mapping and the localization process in the SLAM algorithm was greatly improved in a featureless tunnel environment considering both inclined and flat terrains.

This paper presents a sensor fusion strategy applied for Simultaneous Localization and Mapping (SLAM) in dynamic environments. The designed approach consists of two features: (i) the first one is a fusion module which synthesizes line segments obtained from laser rangefinder and line features extracted from monocular camera. This policy eliminates any pseudo segments that appear from any momentary pause of dynamic objects in laser data. (ii) The second characteristic is a modified multi-sensor point estimation fusion SLAM (MPEF-SLAM) that incorporates two individual Extended Kalman Filter (EKF) based SLAM algorithms: monocular and laser SLAM. The error of the localization in fused SLAM is reduced compared with those of individual SLAM. Additionally, a new data association technique based on the homography transformation matrix is developed for monocular SLAM. This data association method relaxes the pleonastic computation. The experimental results validate the performance of the proposed sensor fusion and data association method.

This paper presents the development of an innovative indoor localization system that leverages a self-rotating inertial sensor for direction estimation and an LSTM-based deep learning model for distance estimation. The primary objective is to enhance the positioning accuracy of two-dimensional moving objects, such as Automatic Guided Vehicles (AGVs), in environments where GPS is unavailable. Traditional indoor localization methods, including wireless, Simultaneous Localization and Mapping (SLAM), and inertial navigation, each have limitations in terms of accuracy and robustness. The proposed system overcomes these limitations in inertial navigation by combining a unique hardware configuration with deep learning techniques. The system’s effectiveness was validated through various performance evaluations, demonstrating significant improvements in direction and distance estimation accuracy. Specifically, the direction error was reduced from 52° to 5° over a 30-minute evaluation period. Additionally, it was confirmed that the average error in estimating the travel distance for straight and rectangular paths was reduced from over 3% with conventional methods to less than 1%.

Simultaneous Localization and Mapping (SLAM) enable autonomous mobile robots to build a map of an unknown environment while estimating their own position within it. Unstructured terrain, dynamic environmental conditions, and sensor limitations, often encountered in off-road areas can limit the effectiveness of SLAM. This review analyzes SLAM methodologies applicable to off-road mobile robots, categorizing them into LiDAR-based, visual, multi-sensor fusion, and learning-based or semantic approaches. Each category is examined in terms of algorithmic principles, performance characteristics, and suitability for varying terrain and environmental conditions. Furthermore, to assess the performance of these categories evaluation metrics are utilized, including accuracy, drift rate, robustness, computational efficiency, and benchmarking datasets. The comparative analysis highlights trade-offs between geometric precision, adaptability, and computational demands, with multi-sensor fusion and semantic integration. Real-time operation under limited onboard computation, scalability to large unstructured terrains, resilience in GPS-denied and feature-scarce environments, and integration with autonomous navigation systems are some of the identified research gaps. The findings emphasize the need for hybrid, computation-aware SLAM frameworks and standardized off-road benchmarks to accelerate the deployment of reliable autonomous systems in challenging outdoor environments.