Automated lane change behavior prediction and environmental perception based on SLAM technology

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.

In recent years, the use of autonomous mobile robots has increased. SLAM (Simultaneous Localization and Mapping) technology is where an autonomous mobile robot estimates its own position and simultaneously maps the surrounding environment. It is widely used in robots that require autonomous movement. However, it is heavily burdened by computational tasks related to various types of image processing and calculations of physical environment recognition, which makes the system slow down due to limited hardware resources such as embedded systems. Various offload methods have been proposed, however they have not been applied to Robot Operating System (ROS) + SLAM applications and there are few examples of robot verification. To avoid the heavy load on the devices, we propose a method that takes the heavily load to the edge by using the prediction model based on the system resource information for a certain period related to SLAM processing. Based on preliminary experiences, we firstly develop a prediction model using system resource consumption data obtained from an actual SLAM execution and evaluate the prediction accuracy. The result shows that the accuracy varies as memory consumption increases, however the accuracy was high and useful in determining offload timing.

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.

Mobile robots are tasked with jobs that assist or replace human work. With the development of technology, mobile robots have had a significant impact on human productive life. As for mobile robots, the core technology is the location awareness of itself. Therefore, this paper analyses the current development state of simultaneous localization and mapping (SLAM) technology applied to mobile robots. First, the overall process and key technologies were explained in detail. Subsequently, the classical algorithms developed in SLAM technology so far are classified based on the characteristics of information-aware devices in terms of methods as well as a more detailed description and comparative analysis of the algorithms. Finally, this paper analyses the current development status of SLAM and the remaining problems from the key performance indicators of SLAM technology, and interprets the future development trend of SLAM technology applied to mobile robots.

This research work is aimed at application of sensing and mapping technologies that have been developed in mobile robotics, so as to perform equipment measurements of forest trees. This research work utilizes a small sized laser scanner and SLAM (Simultaneous Localization and Mapping) technology for the problem of performing forest mensurements. One of the key pieces of information required for forest management, especially in artificial forests, is accurate records of the tree sizes and the standing timber volume per unit area. The authors have made measurement equipment fore a pre-production trial which consists of small sized laser range scanners with a rotating (scanning) mechanism of them. SLAM and related technologies are applied for the information extraction. In the development of SLAM algorithm for this application, the sparseness of the standing trees and the inclination of the forest floor are considered. After performing the SLAM and obtaining a map based on the data from several measurement points, we can obtain useful information including a map of the standing trees, the diameter at chest height of every tree, and the height at crown base (length of the clear bole). The authors will present the experimental results from the forest including the map and the measured tree sizes.

Simultaneous Localization and Mapping (SLAM) is a fundamental problem in mobile robotics. However, the majority of Visual SLAM algorithms assume a static scenario, limiting their applicability in real-world environments. Dealing with dynamic content in Visual SLAM is still an open problem, with solutions usually relying on direct or feature-based methods. Deep learning techniques can improve the SLAM solution in environments with a priori dynamic objects, providing high-level information of the scene. This paper presents a new approach to SLAM in human populated environments using deep learning-based techniques. The system is built on ORB-SLAM2, a state-of-the-art SLAM system. The proposed methodology is evaluated using a benchmark dataset, outperforming other Visual SLAM methods in highly dynamic scenarios.

Simultaneous Localization and Mapping (SLAM) is an important method for autonomous positioning and navigation of robots. SLAM application in underwater area has become an active field of research. Firstly, this paper presents the research status of SLAM technology follow by its application on underwater vehicles. Then, the implementation methods and main difficulties of underwater SLAM are explained. Finally, the research trend of underwater SLAM in the future is discussed.

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.

Simultaneous localization and mapping (SLAM) techniques are widely researched, since they allow the simultaneous creation of a map and the sensors’ pose estimation in an unknown environment. Visual-based SLAM techniques play a significant role in this field, as they are based on a low-cost and small sensor system, which guarantees those advantages compared to other sensor-based SLAM techniques. The literature presents different approaches and methods to implement visual-based SLAM systems. Among this variety of publications, a beginner in this domain may find problems with identifying and analyzing the main algorithms and selecting the most appropriate one according to his or her project constraints. Therefore, we present the three main visual-based SLAM approaches (visual-only, visual-inertial, and RGB-D SLAM), providing a review of the main algorithms of each approach through diagrams and flowcharts, and highlighting the main advantages and disadvantages of each technique. Furthermore, we propose six criteria that ease the SLAM algorithm’s analysis and consider both the software and hardware levels. In addition, we present some major issues and future directions on visual-SLAM field, and provide a general overview of some of the existing benchmark datasets. This work aims to be the first step for those initiating a SLAM project to have a good perspective of SLAM techniques’ main elements and characteristics.

Current visual simultaneous localization and mapping (SLAM) systems have demonstrated commendable efficacy in static environments. However, the presence of dynamic objects in real-world settings frequently leads to system discrepancies, significantly impairing the accuracy and robustness of SLAM systems. Conventional visual SLAM approaches typically utilize epipolar constraints to mitigate the impact of outliers; nevertheless, they encounter limitations when confronted with a substantial number of dynamic or planar moving objects. To tackle these issues, this paper introduces a novel dynamic visual SLAM system, termed DEG-SLAM. Initially, the system employs the YOLOv5 object detection network to identify dynamic objects, subsequently relaying the semantic information to the tracking module. During the tracking phase, both semantic information and epipolar constraints are leveraged to filter out dynamic feature points. To address the challenges posed by the malfunctioning of epipolar constraints in degenerate scenes, DEG-SLAM incorporates a degenerate constraint mechanism aimed at further eliminating dynamic feature points. Furthermore, a reprojection constraint has been introduced to enhance the filtering of absent dynamic feature points that lie outside the detection boxes. Experimental findings reveal that DEG-SLAM significantly improves accuracy and robustness when compared to traditional ORB-SLAM3 in dynamic environments. The performance benefits of DEG-SLAM are particularly evident in degenerate scenarios, thereby affirming its practicality and reliability in complex settings.

Most simultaneous localization and mapping (SLAM) systems assume that SLAM is conducted in a static environment. When SLAM is used in dynamic environments, the accuracy of each part of the SLAM system is adversely affected. We term this problem as dynamic SLAM. In this study, we propose solutions for three main problems in dynamic SLAM: camera tracking, three-dimensional map reconstruction, and loop closure detection. We propose to employ geometry-based method, deep learning-based method, and the combination of them for object segmentation. Using the information from segmentation to generate the mask, we filter the keypoints that lead to errors in visual odometry and features extracted by the CNN from dynamic areas to improve the performance of loop closure detection. Then, we validate our proposed loop closure detection method using the precision-recall curve and also confirm the framework’s performance using multiple datasets. The absolute trajectory error and relative pose error are used as metrics to evaluate the accuracy of the proposed SLAM framework in comparison with state-of-the-art methods. The findings of this study can potentially improve the robustness of SLAM technology in situations where mobile robots work together with humans, while the object-based point cloud byproduct has potential for other robotics tasks.

Building maps is one of the most fundamental tasks for an autonomous robot. This robot should be able to construct a map of the environment and, at the same time, localize itself in it. This problem, known as Simultaneous Localization and Mapping (SLAM), has received great interest in the last years (Leonard & Durrant-Whyte, 1991). In our particular case, the robots build their maps using the FastSLAM algorithm (Montemerlo et al., 2002). The main idea of the FastSLAM algorithm is to separate the two fundamental aspects of the SLAM problem: the estimate of the robot’s pose and the estimate of the map. This algorithm uses a particle set that represents the uncertainty of the robot’s pose (localization problem) meanwhile each particle has its own associated map (several individual estimates of the landmarks conditioned to the robot’s path). The solution to the SLAM problem is performed by means of a sampling and particle generation process, in which the particles whose current observations do not fit with their associated map are eliminated. The FastSLAM algorithm has proved to be robust to false data association and it is able to represent models of non-linear movements in a reliable way (Howard, 2006). In relation to the sensors used to build the maps, many authors use range sensors such as sonar (Kwak et al., 2008; Wijk & Christensen, 2000) or laser (Thrun, 2004; Triebel & Burgard, 2005). Nevertheless, there is an increasing interest on using cameras as sensors. This approach is denoted as visual SLAM (Valls Miro et al., 2006). These devices obtain a higher amount of information from the environment and they are less expensive than laser as well. Furthermore, 3D information can be obtained when using stereo cameras. These are the sensors used in this work. Most visual SLAM approaches are landmark-based. These landmarks consist of a set of distinctive points which are referred to a global reference system. The main advantage of landmark-based maps is the compactness of their representation. By contrast, this kind of maps requires the existence of structures or objects that are distinguishable enough. The map building problem can be solved by a single robot (Moutalier & Chatila, 1989), but it will be more efficiently managed if there is a team of robots, which collaborates in the construction of the map (Howard, 2006). In this case, the space can be divided so that the distances traversed are shorter and thus the odometry errors will be smaller. In this work, 7

PurposeVisual simultaneous localization and mapping (SLAM) has limitations such as sensitivity to lighting changes and lower measurement accuracy. The effective fusion of information from multiple modalities to address these limitations has emerged as a key research focus. This study aims to provide a comprehensive review of the development of vision-based SLAM (including visual SLAM) for navigation and pose estimation, with a specific focus on techniques for integrating multiple modalities.Design/methodology/approachThis paper initially introduces the mathematical models and framework development of visual SLAM. Subsequently, this paper presents various methods for improving accuracy in visual SLAM by fusing different spatial and semantic features. This paper also examines the research advancements in vision-based SLAM with respect to multi-sensor fusion in both loosely coupled and tightly coupled approaches. Finally, this paper analyzes the limitations of current vision-based SLAM and provides predictions for future advancements.FindingsThe combination of vision-based SLAM and deep learning has significant potential for development. There are advantages and disadvantages to both loosely coupled and tightly coupled approaches in multi-sensor fusion, and the most suitable algorithm should be chosen based on the specific application scenario. In the future, vision-based SLAM is evolving toward better addressing challenges such as resource-limited platforms and long-term mapping.Originality/valueThis review introduces the development of vision-based SLAM and focuses on the advancements in multimodal fusion. It allows readers to quickly understand the progress and current status of research in this field.

Simultaneous localization and mapping (SLAM), a pivotal technology in robotics, autonomous vehicles, and surveillance, has gained prominence with the emergence of edge intelligence. Developing energy-efficient, low-latency SLAM systems is essential due to resource constraints and real-time demands. Compute-in-memory (CIM) architectures have been proven to be efficient for matrix multiplications. However, applications for SLAM raise new challenges in memory access and computation aspects: the linear system solving (LS) requires row transformation and causes frequent CIM updates, while the backend optimization causes redundant memory access; back-end optimization dominates SLAM’s computation and requires high precision and high dynamic range. Thus, we propose SLAM-CIM, a visual SLAM backend processor for edge robotics. A dynamic-range-driven-skipping CIM macro is designed to realize energy-efficient floating point (FP)-multiply-and-accumulate (MAC) operations. A preconditional-conjugate-gradient-based in-memory linear solver (PILARS) is designed to achieve LS without additional row transformations. This reduces memory access by 2.08 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times$</tex-math> </inline-formula> and linear-system-solving latency by 3.84 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times$</tex-math> </inline-formula> . SLAM-CIM further minimizes CIM weight updates through incremental bundle adjustment (BA), increasing average CIM utilization by 2.8 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times$</tex-math> </inline-formula> . A silicon prototype is fabricated using 28-nm CMOS technology. The measurements show that SLAM-CIM achieves accurate and efficient SLAM operations with an average energy efficiency of 31.53 TFLOPS/W.

Although tremendous progress has been made in Simultaneous Localization and Mapping (SLAM), the scene rigidity assumption limits wide usage of visual SLAMs in the real-world environment of computer vision, smart robotics and augmented reality. To make SLAM more robust in dynamic environments, outliers on the dynamic objects, including unknown objects, need to be removed from tracking process. To address this challenge, we present a novel real-time visual SLAM system, KMOP-vSLAM, which adds the capability of unsupervised learning segmentation and human detection to reduce the drift error of tracking in indoor dynamic environments. An efficient geometric outlier detection method is proposed, using dynamic information of the previous frames as well as a novel probability model to judge moving objects with the help of geometric constraints and human detection. Outlier features belonging to moving objects are largely detected and removed from tracking. The well-known dataset, TUM, is used to evaluate tracking errors in dynamic scenes where people are walking around. Our approach yields a significantly lower trajectory error compared to state-of-the-art visual SLAMs using an RGB-D camera.