Simultaneous Localization And Mapping Research Articles

SLAM for Indoor Mapping of Wide Area Construction Environments

Abstract. Simultaneous localization and mapping (SLAM), i.e., the reconstruction of the environment represented by a (3D) map and the concurrent pose estimation, has made astonishing progress. Meanwhile, large scale applications aiming at the data collection in complex environments like factory halls or construction sites are becoming feasible. However, in contrast to small scale scenarios with building interiors separated to single rooms, shop floors or construction areas require measures at larger distances in potentially texture less areas under difficult illumination. Pose estimation is further aggravated since no GNSS measures are available as it is usual for such indoor applications. In our work, we realize data collection in a large factory hall by a robot system equipped with four stereo cameras as well as a 3D laser scanner. We apply our state-of-the-art LiDAR and visual SLAM approaches and discuss the respective pros and cons of the different sensor types for trajectory estimation and dense map generation in such an environment. Additionally, dense and accurate depth maps are generated by 3D Gaussian splatting, which we plan to use in the context of our project aiming on the automatic construction and site monitoring.

Extraction of Motion Information from Occupancy Grid Map Using Keystone Transform

Considering the tractability of OGM (Occupancy Grid Map) and its wide use in the dynamic environment representation of mobile robotics, the extraction of motion information from successive OGMs are very important for many tasks, such as SLAM (Simultaneously Localization And Mapping) and DATMO (Detection and Tracking of Moving Object). In this paper, we propose a novel motion extraction method based on the signal transform, called as S-KST (Spatial Keystone Transform), for the motion detection and estimation from successive noisy OGMs. It extends the KST in radar imaging or motion compensation to 1D spatial case (1DS-KST) and 2D spatial case (2DS-KST) combined multiple hypotheses about possible directions of moving obstacles. Meanwhile, the fast algorithm of 2DS-KST based on Chirp Z-Transform (CZT) is also given, which includes five steps, i.e. spatial FFT, directional filtering, CZT, spatial IFFT and Maximal Power Detector (MPD) merging and its computational complexity is proportional to the 2D-FFT. Simulation test results for the point objects and the extended objects show that SKST has a good performance on the extraction of sub-pixel motions in very noisy environment, especially for those slowly moving obstacles.

Simultaneous localization and mapping (SLAM)-based robot localization and navigation algorithm

This research paper presents a comprehensive study of the simultaneous localization and mapping (SLAM) algorithm for robot localization and navigation in unknown environments. The SLAM algorithm is a widely used approach for building a map of an environment and estimating the robot’s position within it, which is especially useful in dynamic and unstructured environments. The paper discusses various SLAM techniques, including the Kalman filter (KF) and GraphSLAM algorithms, and their use in probabilistic estimation of the robot’s position and orientation. The paper also explores different path-planning techniques that can be used with the map created by the SLAM algorithm to generate collision-free paths for the robot to navigate toward its goal. The paper also discusses recent advances in deep learning-based SLAM algorithms and their applications in indoor navigation with ORB and RGB-D cameras. The research concludes that SLAM-based robot localization and navigation algorithms are a promising approach for robots navigating in unstructured environments and present various opportunities for future research.

Telemedicine as a Referral Bridge: A Nurse Practitioner–Led Intervention to Increase Initial Psychiatric Appointment Attendance

Patients with depression and anxiety symptoms present to urgent care settings and receive psychiatric referrals. Referred patients often have difficulty establishing care, leading to low attendance at initial appointments and impacting outcomes. This quasi-experimental study implemented a telemedicine-based intervention in which a nurse practitioner (NP) engaged patients every 2 weeks while awaiting psychiatry. Patients who declined the intervention but consented to follow-up were the comparison group. The intervention group demonstrated an 87% completion rate of psychiatric appointments compared with 0% in the comparison group. The average duration of referral was 63.7 days. Patients with mental illness need support while awaiting psychiatry. Patient engagement from an NP using telemedicine promotes successful transition of patients from urgent care to psychiatry. This NP-led approach promotes referral loop closure and can be used by NPs in various clinical settings, emphasizing positive patient outcomes.

H-SLAM: Hybrid direct–indirect visual SLAM

The recent success of hybrid methods in monocular odometry has led to many attempts to generalize the performance gains to hybrid monocular SLAM. However, most attempts fall short in several respects, with the most prominent issue being the need for two different map representations (local and global maps), with each requiring different, computationally expensive, and often redundant processes to maintain. Moreover, these maps tend to drift with respect to each other, resulting in contradicting pose and scene estimates, and leading to catastrophic failure. In this paper, we propose a novel approach that makes use of descriptor sharing to generate a single inverse depth scene representation. This representation can be used locally, queried globally to perform loop closure, and has the ability to re-activate previously observed map points after redundant points are marginalized from the local map, eliminating the need for separate map maintenance processes. The maps generated by our method exhibit no drift between each other, and can be computed at a fraction of the computational cost and memory footprint required by other monocular SLAM systems. Despite the reduced resource requirements, the proposed approach maintains its robustness and accuracy, delivering performance comparable to state-of-the-art SLAM methods (e.g., LDSO, ORB-SLAM3) on the majority of sequences from well-known datasets like EuRoC, KITTI, and TUM VI. The source code is available at: https://github.com/AUBVRL/fslam_ros_docker.

Simultaneous Localization and Mapping of a Mobile Robot With Stereo Camera Using ORB Features

Simultaneous Localization and Mapping (SLAM) is applied to robots for accurate navigation. The stereo cameras are suitable for visual SLAM as they can give the depth of the visual landmarks and more precise estimations of the robot’s pose. In this paper, we present a survey of SLAM methods, either Bayesian or bioinspired. Then we present a new method of SLAM, which we call stereo Extended Kalman Filter, improving the matching by computing the innovation matrices from the left and the right images. The landmarks are computed from Oriented FAST and Rotated BRIEF (ORB) features for detecting salient points and their descriptors. The covariance matrices of the state and the robot’s map are reduced during the robot’s motion. Experiments are done on the raw images of the Kitti dataset.

LiDAR-based SLAM pose estimation via GNSS graph optimization algorithm

LiDAR simultaneous localization and mapping (SLAM) is widely used in positioning and navigation. By illuminating a series of light spots on the surface of an object, orientation and pose information is obtained. However, improving the accuracy of the pose optimization algorithm without affecting the position information is difficult. Therefore, this study combines the graph optimization algorithm and the Global Navigation Satellite System (GNSS) to optimize the coordinates of the target object in the LiDAR SLAM pose. A GNSS pose estimation algorithm is proposed to show the relationship between positioning algorithms with and without GNSS pose optimization by analyzing the deviation of the distance, level, and height laser point cloud coordinates. The results show that with GNSS pose optimization, the deviations in distance, level, and height are 99% smaller than those without GNSS pose optimization. Furthermore, we demonstrate the effectiveness of the proposed graph optimization algorithm and GNSS optimization of LiDAR SLAM. Finally, this study highlights the directions for the application of wireless communication technology in the field of LiDAR SLAM.

Construction of Three-Dimensional Semantic Maps of Unstructured Lawn Scenes Based on Deep Learning

Traditional automatic gardening pruning robots generally employ electronic fences for the delineation of working boundaries. In order to quickly determine the working area of a robot, we combined an improved DeepLabv3+ semantic segmentation model with a simultaneous localization and mapping (SLAM) system to construct a three-dimensional (3D) semantic map. To reduce the computational cost of its future deployment in resource-constrained mobile robots, we replaced the backbone network of DeepLabv3+, ResNet50, with MobileNetV2 to decrease the number of network parameters and improve recognition speed. In addition, we introduced an efficient channel attention network attention mechanism to enhance the accuracy of the neural network, forming an improved Multiclass MobileNetV2 ECA DeepLabv3+ (MM-ED) network model. Through the integration of this model with the SLAM system, the entire framework was able to generate a 3D semantic point cloud map of a lawn working area and convert it into octree and occupancy grid maps, providing technical support for future autonomous robot operation and navigation. We created a lawn dataset containing 7500 images, using our own annotated images as ground truth. This dataset was employed for experimental purposes. Experimental results showed that the proposed MM-ED network model achieved 91.07% and 94.71% for MIoU and MPA metrics, respectively. Using a GTX 3060 Laptop GPU, the frames per second rate reached 27.69, demonstrating superior recognition performance compared to similar semantic segmentation architectures and better adaptation to SLAM systems.

DMOT-SLAM: visual SLAM in dynamic environments with moving object tracking

Visual simultaneous localization and mapping (SLAM) in dynamic environments has received significant attention in recent years, and accurate segmentation of real dynamic objects is the key to enhancing the accuracy of pose estimation in such environments. In this study, we propose a visual SLAM approach based on ORB-SLAM3, namely dynamic multiple object tracking SLAM (DMOT-SLAM), which can accurately estimate the camera’s pose in dynamic environments while tracking the trajectories of moving objects. We introduce a spatial point correlation constraint and combine it with instance segmentation and epipolar constraint to identify dynamic objects. We integrate the proposed motion check method into DeepSort, an object tracking algorithm, to facilitate inter-frame tracking of dynamic objects. This integration not only enhances the stability of dynamic features detection but also enables the estimation of global motion trajectories for dynamic objects and the construction of object-level semi-dense semantic maps. We evaluate our approach on the public TUM, Bonn, and KITTI dataset, and the results show that our approach has a significant improvement over ORB-SLAM3 in dynamic scenes and performs better compared to other state-of-the-art SLAM approaches. Moreover, experiments in real-world scenarios further substantiate the effectiveness of our approach.

Path Planning for Autonomous Mobile Robot Using Intelligent Algorithms

Machine learning technologies are being integrated into robotic systems faster to enhance their efficacy and adaptability in dynamic environments. The primary goal of this research was to propose a method to develop an Autonomous Mobile Robot (AMR) that integrates Simultaneous Localization and Mapping (SLAM), odometry, and artificial vision based on deep learning (DL). All are executed on a high-performance Jetson Nano embedded system, specifically emphasizing SLAM-based obstacle avoidance and path planning using the Adaptive Monte Carlo Localization (AMCL) algorithm. Two Convolutional Neural Networks (CNNs) were selected due to their proven effectiveness in image and pattern recognition tasks. The ResNet18 and YOLOv3 algorithms facilitate scene perception, enabling the robot to interpret its environment effectively. Both algorithms were implemented for real-time object detection, identifying and classifying objects within the robot’s environment. These algorithms were selected to evaluate their performance metrics, which are critical for real-time applications. A comparative analysis of the proposed DL models focused on enhancing vision systems for autonomous mobile robots. Several simulations and real-world trials were conducted to evaluate the performance and adaptability of these models in navigating complex environments. The proposed vision system with CNN ResNet18 achieved an average accuracy of 98.5%, a precision of 96.91%, a recall of 97%, and an F1-score of 98.5%. However, the YOLOv3 model achieved an average accuracy of 96%, a precision of 96.2%, a recall of 96%, and an F1-score of 95.99%. These results underscore the effectiveness of the proposed intelligent algorithms, robust embedded hardware, and sensors in robotic applications. This study proves that advanced DL algorithms work well in robots and could be used in many fields, such as transportation and assembly. As a consequence of the findings, intelligent systems could be implemented more widely in the operation and development of AMRs.

ADM-SLAM: Accurate and Fast Dynamic Visual SLAM with Adaptive Feature Point Extraction, Deeplabv3pro, and Multi-View Geometry.

Visual Simultaneous Localization and Mapping (V-SLAM) plays a crucial role in the development of intelligent robotics and autonomous navigation systems. However, it still faces significant challenges in handling highly dynamic environments. The prevalent method currently used for dynamic object recognition in the environment is deep learning. However, models such as Yolov5 and Mask R-CNN require significant computational resources, which limits their potential in real-time applications due to hardware and time constraints. To overcome this limitation, this paper proposes ADM-SLAM, a visual SLAM system designed for dynamic environments that builds upon the ORB-SLAM2. This system integrates efficient adaptive feature point homogenization extraction, lightweight deep learning semantic segmentation based on an improved DeepLabv3, and multi-view geometric segmentation. It optimizes keyframe extraction, segments potential dynamic objects using contextual information with the semantic segmentation network, and detects the motion states of dynamic objects using multi-view geometric methods, thereby eliminating dynamic interference points. The results indicate that ADM-SLAM outperforms ORB-SLAM2 in dynamic environments, especially in high-dynamic scenes, where it achieves up to a 97% reduction in Absolute Trajectory Error (ATE). In various highly dynamic test sequences, ADM-SLAM outperforms DS-SLAM and DynaSLAM in terms of real-time performance and accuracy, proving its excellent adaptability.

Improvement methods for feature matching in Basalt SLAM

Abstract Visual simultaneous localization and mapping (SLAM) is widely used to provide localization information for mobile robots, but most visual SLAM algorithms lack effective data association methods, resulting in unstable position tracking results. In this paper, relevant improvement schemes for global feature matching in the Basalt SLAM algorithm are proposed to enhance the effectiveness of its data association method. Firstly, the more efficient box average difference (BAD) descriptor is used instead of the ORB descriptor. Secondly, secondary matching is performed by narrowing the matching range with the epipolar geometric constraint to expand matching pairs. Finally, grid-based motion statistics (GMS) and histogram-based rotation consistency constraints are introduced to eliminate false matches. Experiments on the EuRoC dataset demonstrate that the proposed methods significantly enhance the effectiveness and stability of visual data association and improve the localization accuracy of the Basalt SLAM system.

Research on tightly coupled SLAM method based on LiDAR/IMU

Abstract Aiming at the problems of insufficient accuracy and serious computational time-consumption of mainstream laser odometry schemes, this paper proposes an innovative solution, where the front end is based on the idea of iterative Kalman filtering, which realizes the tight coupling of inertial measurement unit (IMU) and LiDAR to obtain high-precision position and attitude estimation. The back-end optimization employs a factor graph approach, incorporating laser odometry, IMU preintegration, and loop closure detection factors. In comparison with state-of-the-art laser SLAM algorithms, the SLAM algorithm proposed in this paper reduces the absolute position error of trajectories by 9.61%, 91.03%, and 94.89% compared to Fast-lio2, Lego-loam, and a-loam, respectively.

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

In the automatic driving system, the external environment of the vehicle is perceived, in fact, there is also a perception sensor that has been silently dedicated in the system, that is, the positioning module. This paper explores the role of SLAM (Simultaneous Localization and Mapping) technology in autonomous vehicles, particularly in automatic lane change behavior prediction and environment perception. It emphasizes the limitations of traditional methods and the advantages of SLAM, especially visual SLAM, for accurate positioning and mapping. The discussion covers SLAM fundamentals, challenges, and the significance of visual SLAM's higher perception ability. Case studies on Waymo and Tesla illustrate the application of visual SLAM in achieving high-precision navigation and lane change prediction. The paper concludes by highlighting future research directions to enhance the intelligence and adaptability of automated lane change systems through advancements in AI and sensor technology, alongside optimizing SLAM algorithms for reliable driving in various scenarios.

Integrating artificial intelligence with SLAM technology for robotic navigation and localization in unknown environments

In the era of advancing technology, unmanned inspection robots have become indispensable for their efficiency, precision, and safety. Key to their autonomous operation is Simultaneous Localization and Mapping (SLAM) technology, which allows robots to navigate and create maps of unknown environments in real-time. This article explores the integration of SLAM with artificial intelligence, highlighting its role in robotic navigation, localization, and obstacle avoidance. Specifically, we delve into SLAM's principles, its implementation with LiDAR technology, and its application in autonomous robot localization. Furthermore, we introduce a collaborative mapping algorithm based on ORB-SLAM3, enhancing map construction efficiency and real-time performance. Through our exploration, we illustrate the transformative potential of SLAM technology, paving the way for safer and more efficient robotic inspection systems across various industries.

A Low-Cost 3D SLAM System Integration of Autonomous Exploration Based on Fast-ICP Enhanced LiDAR-Inertial Odometry

Advancements in robotics and mapping technology have spotlighted the development of Simultaneous Localization and Mapping (SLAM) systems as a key research area. However, the high cost of advanced SLAM systems poses a significant barrier to research and development in the field, while many low-cost SLAM systems, operating under resource constraints, fail to achieve high-precision real-time mapping and localization, rendering them unsuitable for practical applications. This paper introduces a cost-effective SLAM system design that maintains high performance while significantly reducing costs. Our approach utilizes economical components and efficient algorithms, addressing the high-cost barrier in the field. First, we developed a robust robotic platform based on a traditional four-wheeled vehicle structure, enhancing flexibility and load capacity. Then, we adapted the SLAM algorithm using the LiDAR-inertial Odometry framework coupled with the Fast Iterative Closest Point (ICP) algorithm to balance accuracy and real-time performance. Finally, we integrated the 3D multi-goal Rapidly exploring Random Tree (RRT) algorithm with Nonlinear Model Predictive Control (NMPC) for autonomous exploration in complex environments. Comprehensive experimental results confirm the system’s capability for real-time, autonomous navigation and mapping in intricate indoor settings, rivaling more expensive SLAM systems in accuracy and efficiency at a lower cost. Our research results are published as open access, facilitating greater accessibility and collaboration.

Simultaneous Localization and Mapping (SLAM) for warehouse applications

Simultaneous Localization and Mapping (SLAM) for warehouse applications

Real-Time Estimation of Tree Position, Tree Height, and Tree Diameter at Breast Height Point, Using Smartphones Based on Monocular SLAM

Precisely estimating the position, diameter at breast height (DBH), and height of trees is essential in forest resource inventory. Augmented reality (AR)-based devices help overcome the issue of inconsistent global point cloud data under thick forest canopies with insufficient Global Navigation Satellite System (GNSS) coverage. Although monocular simultaneous localization and mapping (SLAM) is one of the current mainstream systems, there is still no monocular SLAM solution for forest resource inventories, particularly for the precise measurement of inclined trees. We developed a forest plot survey system based on monocular SLAM that utilizes array cameras and Inertial Measurement Unit (IMU) sensors provided by smartphones, combined with augmented reality technology, to achieve a real-time estimation of the position, DBH, and height of trees within forest plots. Our results from the tested plots showed that the tree position estimation is unbiased, with an RMSE of 0.12 m and 0.11 m in the x-axis and y-axis directions, respectively; the DBH estimation bias is −0.17 cm (−0.65%), with an RMSE of 0.83 cm (3.59%), while the height estimation bias is −0.1 m (−0.95%), with an RMSE of 0.99 m (5.38%). This study will be useful in designing an algorithm to estimate the DBH and position of inclined trees using point clouds constrained by sectional planes at the breast height of the trunk, developing an algorithm to estimate the height of inclined trees utilizing the relationship between rays and plane positions, and providing observers with visual measurement results using augmented reality technology, allowing them to judge the accuracy of the estimates intuitively. Clearly, this system has significant potential applications in forest resource management and ecological research.

Design and Implementation of a Hybrid-Driven Soft Robot

Currently, soft robots alone cannot obtain the same operating speed as rigid robots, while rigid robots are not safe enough for human-robot interaction. To address this problem, this paper describes a hybrid robot system that combines both rigid and flexible systems for unknown domain exploration. The system consists of a four-wheeled robot chassis and a cylindrical pneumatic soft actuator, and finally, a computer is used to coordinate and control both. The hardware of the robot system is designed, a bending motion model is proposed, and SOFA framework is used to carry out finite element simulation (FEM) to verify the reasonableness of the design; linear motion speeds of up to 0.5 m/s, higher than the existing soft robots investigated, were verified experimentally separately after carrying the new module, and steering ability was retained; and the robot carrying the navigation module is verified to have a certain map building and localization function through the construction of the simultaneous localization and mapping (SLAM) experimental platform. The hybrid robot introduced in this paper can move quickly on flat terrain and can use its soft part to avoid wear and tear.

Development of Unmanned Aerial Vehicle Navigation and Warehouse Inventory System Based on Reinforcement Learning

In this paper, we present the exploration of indoor positioning technologies for UAVs, as well as navigation techniques for path planning and obstacle avoidance. The objective was to perform warehouse inventory tasks, using a drone to search for barcodes or markers to identify objects. For the indoor positioning techniques, we employed visual-inertial odometry (VIO), ultra-wideband (UWB), AprilTag fiducial markers, and simultaneous localization and mapping (SLAM). These algorithms included global positioning, local positioning, and pre-mapping positioning, comparing the merits and drawbacks of various techniques and trajectories. For UAV navigation, we combined the SLAM-based RTAB-map indoor mapping and navigation path planning of the ROS for indoor environments. This system enabled precise drone positioning indoors and utilized global and local path planners to generate flight paths that avoided dynamic, static, unknown, and known obstacles, demonstrating high practicality and feasibility. To achieve warehouse inventory inspection, a reinforcement learning approach was proposed, recognizing markers by adjusting the UAV’s viewpoint. We addressed several of the main problems in inventory management, including efficiently planning of paths, while ensuring a certain detection rate. Two reinforcement learning techniques, AC (actor–critic) and PPO (proximal policy optimization), were implemented based on AprilTag identification. Testing was performed in both simulated and real-world environments, and the effectiveness of the proposed method was validated.