Mobile Robot Simultaneous Localization And Mapping Research Articles

Comprehensive Performance Evaluation between Visual SLAM and LiDAR SLAM for Mobile Robots: Theories and Experiments

SLAM (Simultaneous Localization and Mapping), primarily relying on camera or LiDAR (Light Detection and Ranging) sensors, plays a crucial role in robotics for localization and environmental reconstruction. This paper assesses the performance of two leading methods, namely ORB-SLAM3 and SC-LeGO-LOAM, focusing on localization and mapping in both indoor and outdoor environments. The evaluation employs artificial and cost-effective datasets incorporating data from a 3D LiDAR and an RGB-D (color and depth) camera. A practical approach is introduced for calculating ground-truth trajectories and during benchmarking, reconstruction maps based on ground truth are established. To assess the performance, ATE and RPE are utilized to evaluate the accuracy of localization; standard deviation is employed to compare the stability during the localization process for different methods. While both algorithms exhibit satisfactory positioning accuracy, their performance is suboptimal in scenarios with inadequate textures. Furthermore, 3D reconstruction maps established by the two approaches are also provided for direct observation of their differences and the limitations encountered during map construction. Moreover, the research includes a comprehensive comparison of computational performance metrics, encompassing Central Processing Unit (CPU) utilization, memory usage, and an in-depth analysis. This evaluation revealed that Visual SLAM requires more CPU resources than LiDAR SLAM, primarily due to additional data storage requirements, emphasizing the impact of environmental factors on resource requirements. In conclusion, LiDAR SLAM is more suitable for the outdoors due to its comprehensive nature, while Visual SLAM excels indoors, compensating for sparse aspects in LiDAR SLAM. To facilitate further research, a technical guide was also provided for the researchers in related fields.

Adaptive path planning for unknown environment monitoring

The purpose of this paper is to offer a unique adaptive path planning framework to address a new challenge known as the Unknown environment Persistent Monitoring Problem (PMP). To identify the unknown events’ occurrence location and likelihood, an unmanned ground vehicle (UGV) equipped with a Light Detection and Ranging (LIDAR) and camera is used to record such events in agriculture land. A certain level of detecting capability must be the distinct monitoring priority in order to keep track of them to a certain distance. First, to formulate a model, we developed an event-oriented modelling strategy for unknown environment perception and the effect is enumerated by uncertainty, which takes into account the sensor’s detection capabilities, the detection interval, and monitoring weight. A mobile robot scheme utilizing LIDAR on integrative approach was created and experiments were carried out to solve the high equipment budget of Simultaneous Localization and Mapping (SLAM) for robotic systems. To map an unfamiliar location using the robotic operating system (ROS), the 3D visualization tool for Robot Operating System (RVIZ) was utilized, and GMapping software package was used for SLAM usage. The experimental results suggest that the mobile robot design pattern is viable to produce a high-precision map while lowering the cost of the mobile robot SLAM hardware. From a decision-making standpoint, we built a hybrid algorithm HSAStar (Hybrid SLAM & A Star) algorithm for path planning based on the event oriented modelling, allowing a UGV to continually monitor the perspectives of a path. The simulation results and analyses show that the proposed strategy is feasible and superior. The performance of the proposed hyb SLAM-A Star-APP method provides 34.95%, 27.38%, 33.21% and 29.68% lower execution time, 26.36%, 29.64% and 29.67% lower map duration compared with the existing methods, such as ACO-APF-APP, APFA-APP, GWO-APP and PSO-APP.

Efficient Hardware Accelerator Design of Non-Linear Optimization Correlative Scan Matching Algorithm in 2D LiDAR SLAM for Mobile Robots.

Simultaneous localization and mapping (SLAM) is the major solution for constructing or updating a map of an unknown environment while simultaneously keeping track of a mobile robot's location. Correlative Scan Matching (CSM) is a scan matching algorithm for obtaining the posterior distribution probability for the robot's pose in SLAM. This paper combines the non-linear optimization algorithm and CSM algorithm into an NLO-CSM (Non-linear Optimization CSM) algorithm for reducing the computation resources and the amount of computation while ensuring high calculation accuracy, and it presents an efficient hardware accelerator design of the NLO-CSM algorithm for the scan matching in 2D LiDAR SLAM. The proposed NLO-CSM hardware accelerator utilizes pipeline processing and module reusing techniques to achieve low hardware overhead, fast matching, and high energy efficiency. FPGA implementation results show that, at 100 MHz clock, the power consumption of the proposed hardware accelerator is as low as 0.79 W, while it performs a scan match at 8.98 ms and 7.15 mJ per frame. The proposed design outperforms the ARM-A9 dual-core CPU implementation with a 92.74% increase and 90.71% saving in computing speed and energy consumption, respectively. It has also achieved 80.3% LUTs, 84.13% FFs, and 20.83% DSPs saving, as well as an 8.17× increase in frame rate and 96.22% improvement in energy efficiency over a state-of-the-art hardware accelerator design in the literature. ASIC implementation in 65 nm can further reduce the computing time and energy consumption per scan to 5.94 ms and 0.06 mJ, respectively, which shows that the proposed NLO-CSM hardware accelerator design is suitable for resource-limited and energy-constrained mobile and micro robot applications.

Loop Closure Detection Based on Residual Network and Capsule Network for Mobile Robot.

Loop closure detection based on a residual network (ResNet) and a capsule network (CapsNet) is proposed to address the problems of low accuracy and poor robustness for mobile robot simultaneous localization and mapping (SLAM) in complex scenes. First, the residual network of a feature coding strategy is introduced to extract the shallow geometric features and deep semantic features of images, reduce the amount of image noise information, accelerate the convergence speed of the model, and solve the problems of gradient disappearance and network degradation of deep neural networks. Then, the dynamic routing mechanism of the capsule network is optimized through the entropy peak density, and a vector is used to represent the spatial position relationship between features, which can improve the ability of image feature extraction and expression to optimize the overall performance of networks. Finally, the optimized residual network and capsule network are fused to retain the differences and correlations between features, and the global feature descriptors and feature vectors are combined to calculate the similarity of image features for loop closure detection. The experimental results show that the proposed method can achieve loop closure detection for mobile robots in complex scenes, such as view changes, illumination changes, and dynamic objects, and improve the accuracy and robustness of mobile robot SLAM.

Grey Wolf Resampling-Based Rao-Blackwellized Particle Filter for Mobile Robot Simultaneous Localization and Mapping

An artificial intelligent grey wolf optimizer (GWO)-assisted resampling scheme is applied to the Rao-Blackwellized particle filter (RBPF) in the simultaneous localization and mapping (SLAM). By doing this, we can make the diversity of the particles resampling and then obtain a better localization accuracy and fast convergence to realize indoor mobile robot SLAM. In addition, we propose an adaptive local data association (Range-SLAM) scheme to improve the computational efficiency for the algorithm of the nearest neighbor (NN) data association in the iteration of the RBPF prediction. Through the experiment and simulations, the proposed SLAM schemes have fast convergence, accuracy, and heuristics. Therefore, the improved RBPF and new data association schemes presented in this paper can provide a feasible method for the indoor mobile robot SLAM.

SLAM closed-loop detection method of 2D laser fusion vision in a larger space

In the process of implementing SLAM (Simultaneous-Localization-and-Mapping) for mobile robots, using a separate 2D lidar to build a map in a larger space will lead to inaccurate mapping and missed closed-loop detection. Therefore, an improved 2D laser fusion vision closed-loop detection algorithm is proposed. The algorithm first detects the loop through the visual information obtained by the camera, compares the similarity between the current frame and the key frame, and continuously selects a series of closed-loop frames to be selected near the current frame, and then filters the final closed-loop frame through geometric verification More matching points can be obtained through transformation matrix projection, and the matching points can optimize the transformation matrix. If the current frame and the closed-loop candidate frame get enough common-view space points, then the closed-loop detection is determined to be successful. Finally, the visual loop information is fed back to the laser, and the robot’s pose is adjusted to achieve global optimization. In the experimental environment, it is verified that the laser fusion vision effectively solves the problems such as the lack of robustness of the single laser closed loop detection.

Simultaneous Localization and Mapping Based on Kalman Filter and Extended Kalman Filter

For more than two decades, the issue of simultaneous localization and mapping (SLAM) has gained more attention from researchers and remains an influential topic in robotics. Currently, various algorithms of the mobile robot SLAM have been investigated. However, the probability-based mobile robot SLAM algorithm is often used in the unknown environment. In this paper, the authors proposed two main algorithms of localization. First is the linear Kalman Filter (KF) SLAM, which consists of five phases, such as (a) motionless robot with absolute measurement, (b) moving vehicle with absolute measurement, (c) motionless robot with relative measurement, (d) moving vehicle with relative measurement, and (e) moving vehicle with relative measurement while the robot location is not detected. The second localization algorithm is the SLAM with the Extended Kalman Filter (EKF). Finally, the proposed SLAM algorithms are tested by simulations to be efficient and viable. The simulation results show that the presented SLAM approaches can accurately locate the landmark and mobile robot.

Active SLAM for Mobile Robots With Area Coverage and Obstacle Avoidance

In this article, we present an active simultaneous localization and mapping (SLAM) framework for a mobile robot to obtain a collision-free trajectory with good performance in SLAM uncertainty reduction and in an area coverage task. Based on a model predictive control framework, these two tasks are solved by the introduction of a control switching mechanism. For SLAM uncertainty reduction, graph topology is used to approximate the original problem as a constrained nonlinear least squares problem. A convex half-space representation is applied to relax nonconvex spatial constraints that represent obstacle avoidance. Using convex relaxation, the problem is solved by a convex optimization method and a rounding procedure based on singular value decomposition. The area coverage task is addressed with a sequential quadratic programming method. A submap joining approach, called linear SLAM, is used to address the associated challenges of avoiding local minima, minimizing control switching, and potentially high computational cost. Finally, various simulations and experiments using an aerial robot are presented that verify the effectiveness of the proposed method, showing that our method produces a more accurate SLAM result and is more computationally efficient compared with multiple existing methods.

Autonomous Indoor Exploration Via Polygon Map Construction and Graph-Based SLAM Using Directional Endpoint Features

In this paper, a novel 2-D laser-based autonomous exploration approach for mobile robots is proposed, which is based on a novel polygon map construction approach and graph-based simultaneous localization and mapping (SLAM) with directional endpoint features. This approach is composed of three modules: graph-based SLAM using directional endpoint features, polygon map construction, and exploration. Different from existing approaches in the field of 2-D SLAM, the newly proposed 2-D graph-SLAM is based on 3-D “directional endpoint” features; on this basis, a well-known data structure “circular-doubly linked list” is applied to construct a novel polygon map for navigation. Note that it is efficient for circular-doubly linked list to initialize and update the polygon map. In addition, we propose a new information entropy calculation approach to quantify the entropy of the polygon map. Then for each candidate goal, we could obtain corresponding information gain and make next decision through collision detection. Comparative experimental results with respect to the well-known Gmapping and Karto SLAM are presented to show superior performance of the proposed graph-based SLAM. The autonomous exploration experiments in the office and hallway environments show the effectiveness of the proposed approach for robotic mapping and exploration tasks. Note to Practitioners —This paper is motivated by the challenges of autonomous exploration for mobile robots. We suggest a novel autonomous exploration approach through directional endpoint feature-based graph simultaneous localization and mapping (SLAM) and polygon map construction. This newly proposed approach does not require any artificial landmark or underlying occupancy grid map, which is easy to implement. The experiments are carried out in the office and hallway environments, including graph-based SLAM and autonomous exploration for mobile robots. The experimental results show the effectiveness of the proposed autonomous exploration framework. In the future research, we will address autonomous exploration in more challenging dynamic environments.

Least-square Matching for Mobile Robot SLAM Based on Line-segment Model

This study proposes an efficient simultaneous localization and mapping (SLAM) algorithm for a mobile robot. The proposed algorithm consists of line-segment feature extraction from a set of points measured by a LIDAR, association and matching between the line-segments and a map database for position estimation, and the registration of the line-segments into the map database for the incremental construction of the map database. The line-segment features help reduce the amount of data required for map representation. The matching algorithm for position estimation is efficient in computation owing to the use of a number of inliers as the weights in the least-squares method. Experiments are conducted to demonstrate the performance of the proposed SLAM algorithm, and the results show that the proposed algorithm is effective in the map representation and the localization of a mobile robot.

Improved Rao-Blackwellized H∞ filter based mobile robot SLAM

For the problems of estimation accuracy, inconsistencies and robustness in mobile robot simultaneous localization and mapping (SLAM), a novel SLAM based on improved Rao-Blackwellized H∞ particle filter (IRBHF-SLAM) algorithm is proposed. The iterated unscented H∞ filter (IUHF) is utilized to accurately calculate the importance density function, repeatedly correcting the state mean and the covariance matrix by the iterative update method. The laser sensor's observation information is introduced into sequential importance sampling routine. It can avoid the calculation of Jacobian matrix and linearization error accumulation; meanwhile, the robustness of the algorithm is enhanced. IRBHF-SLAM is compared with FastSLAM2.0 and the unscented FastSLAM (UFastSLAM) under different noises in simulation experiments. Results show the algorithm can improve the estimation accuracy and stability. The improved approach, based on the robot operation system (ROS), runs on the Pioneer3-DX robot equipped with a HOKUYO URG-04LX (URG) laser range finder. Experimental results show the improved algorithm can reduce the required number of particles and the operating time; and create online 2 dimensional (2-D) grid-map with high precision in different environments.

Enhanced SLAM for Autonomous Mobile Robots using Unscented Kalman Filter and Neural Network

The novel method of mobile robot Simultaneous Localization And Mapping (SLAM), which is implemented by optimized Unscented Kalman Filter (UKF) Via a Radial Basis Function (RBF) for autonomous robot in unknown indoor environment is proposed. For atone the Unscented Kalman Filter based SLAM errors intrinsically caused by its linearization process, the Radial Basis Function Network is composed with Unscented Kalman Filter. A mobile robot localizes itself autonomously and makes a map simultaneously while it is tracking in an unknown environment. The offered approach has some benefits in handling a robotic system with nonlinear movements because of the learning feature of the Radial Basis Function. The simulation results show the powers and effectiveness of the proposed algorithm comparing with a Standard UKF.

Sensor Technologies and Simultaneous Localization and Mapping (SLAM)

This paper presents a comprehensive review on sensor modalities currently in used for solving the Simultaneous Localization and Mapping (SLAM) problem. The review focuses on SLAM for mobile robots in a variety of environments. The strengths and weaknesses of acoustic modality sensors such as ultrasonic and sonar sensors, laser range finders, visual sensors such as stereo vision sensors, and RGB-D sensors like the Microsoft Kinect and the Asus Xtion Pro Live are compared based on current usage in published research papers. Based on this review, we propose that RGB-D sensors have unique advantages which make them particularly suitable for SLAM problems.

Mobile robot SLAM method based on multi-agent particle swarm optimized particle filter

To overcome particle impoverishment, a simultaneous localization and mapping (SLAM) method based on multi-agent particle swarm optimized particle filter (MAPSOPF) was presented by introducing the idea of multi-agent to the particle swarm optimized particle filter (PSOPF) which is an algorithm for SLAM. In MAPSOPF, agents can communicate and compete with each other and learn from each other. The MAPSOPF algorithm can update the prediction of particle, adjust the proposal distribution of particles, improve localization precision and fault tolerance, and propel the particles to concentrate on the robot's true pose. Compared with standard particle filter (PF), the proposed method can achieve better SLAM precision by fewer particles. Simulations verify its effectiveness and feasibility.

A Novel Measure of Uncertainty for Mobile Robot SLAM with Rao—Blackwellized Particle Filters

Rao—Blackwellized particle filters (RBPFs) are an implementation of sequential Bayesian filtering that has been successfully applied to mobile robot simultaneous localization and mapping (SLAM) and exploration. Measuring the uncertainty of the distribution estimated by a RBPF is required for tasks such as information gain-guided exploration or detecting loop closures in nested loop environments. In this paper we propose a new measure that takes the uncertainty in both the robot path and the map into account. Our approach relies on the entropy of the expected map (EM) of the RBPF, a new variable built by integrating the map hypotheses from all of the particles. Unlike previous works that use the joint entropy of the RBPF for active exploration, our proposal is better suited to detect opportunities to close loops, a key aspect to reduce the robot path uncertainty and consequently to improve the quality of the maps being built. We provide a theoretical discussion and experimental results with real data that support our claims.