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This paper aims to introduce an efficient active-simultaneous localization and mapping (SLAM) approach for rover navigation, future planetary rover exploration mission requires the rover to automatically localize itself with high accuracy.

A three-dimensional (3D) feature detection method is first proposed to extract salient features from the observed point cloud, after that, the salient features are employed as the candidate destinations for re-visiting under SLAM structure, followed by a path planning algorithm integrated with SLAM, wherein the path length and map utility are leveraged to reduce the growth rate of state estimation uncertainty.

The proposed approach is able to extract distinguishable 3D landmarks for feature re-visiting, and can be naturally integrated with any SLAM algorithms in an efficient manner to improve the navigation accuracy.

This paper proposes a novel active-SLAM structure for planetary rover exploration mission, the salient feature extraction method and active revisit patch planning method are validated to improve the accuracy of pose estimation.

This paper aims to use the Monodepth method to improve the prediction speed of identifying the obstacles and proposes a Probability Dueling DQN algorithm to optimize the path of the agent, which can reach the destination more quickly than the Dueling DQN algorithm. Then the path planning algorithm based on Probability Dueling DQN is combined with FastSLAM to accomplish the autonomous navigation and map the environment.

This paper proposes an active simultaneous localization and mapping (SLAM) framework for autonomous navigation under an indoor environment with static and dynamic obstacles. It integrates a path planning algorithm with visual SLAM to decrease navigation uncertainty and build an environment map.

The result shows that the proposed method offers good performance over existing Dueling DQN for navigation uncertainty under the indoor environment with different numbers and shapes of the static and dynamic obstacles in the real world field.

This paper proposes a novel active SLAM framework composed of Probability Dueling DQN that is the improved path planning algorithm based on Dueling DQN and FastSLAM. This framework is used with the Monodepth depth image prediction method with faster prediction speed to realize autonomous navigation in the indoor environment with different numbers and shapes of the static and dynamic obstacles.

Multi-robot laser-based simultaneous localization and mapping (SLAM) in large-scale environments is an essential but challenging issue in mobile robotics, especially in situations wherein no prior knowledge is available between robots. Moreover, the cumulative errors of every individual robot exert a serious negative effect on loop detection and map fusion. To address these problems, this paper aims to propose an efficient approach that combines laser and vision measurements.

A multi-robot visual laser-SLAM is developed to realize robust and efficient SLAM in large-scale environments; both vision and laser loop detections are integrated to detect robust loops. A method based on oriented brief (ORB) feature detection and bag of words (BoW) is developed, to ensure the robustness and computational effectiveness of the multi-robot SLAM system. A robust and efficient graph fusion algorithm is proposed to merge pose graphs from different robots.

The proposed method can detect loops more quickly and accurately than the laser-only SLAM, and it can fuse the submaps of each single robot to promote the efficiency, accuracy and robustness of the system.

Compared with the state of art of multi-robot SLAM approaches, the paper proposed a novel and more sophisticated approach. The vision-based and laser-based loops are integrated to realize a robust loop detection. The ORB features and BoW technologies are further utilized to gain real-time performance. Finally, random sample consensus and least-square methodologies are used to remove the outlier loops among robots.

Existing Simultaneous Localization and Mapping (SLAM) algorithms have been relatively well developed. However, when in complex dynamic environments, the movement of the dynamic points on the dynamic objects in the image in the mapping can have an impact on the observation of the system, and thus there will be biases and errors in the position estimation and the creation of map points. The aim of this paper is to achieve more accurate accuracy in SLAM algorithms compared to traditional methods through semantic approaches.

In this paper, the semantic segmentation of dynamic objects is realized based on U-Net semantic segmentation network, followed by motion consistency detection through motion detection method to determine whether the segmented objects are moving in the current scene or not, and combined with the motion compensation method to eliminate dynamic points and compensate for the current local image, so as to make the system robust.

Experiments comparing the effect of detecting dynamic points and removing outliers are conducted on a dynamic data set of Technische Universität München, and the results show that the absolute trajectory accuracy of this paper's method is significantly improved compared with ORB-SLAM3 and DS-SLAM.

In this paper, in the semantic segmentation network part, the segmentation mask is combined with the method of dynamic point detection, elimination and compensation, which reduces the influence of dynamic objects, thus effectively improving the accuracy of localization in dynamic environments.

To make the robot that have real autonomous ability is always the goal of mobile robot research. For mobile robots, simultaneous localization and mapping (SLAM) research is no longer satisfied with enabling robots to build maps by remote control, more needs will focus on the autonomous exploration of unknown areas, which refer to the low light, complex spatial features and a series of unstructured environment, lick underground special space (dark and multiintersection). This study aims to propose a novel robot structure with mapping and autonomous exploration algorithms. The experiment proves the detection ability of the robot.

A small bio-inspired mobile robot suitable for underground special space (dark and multiintersection) is designed, and the control system is set up based on STM32 and Jetson Nano. The robot is equipped with double laser sensor and Ackerman chassis structure, which can adapt to the practical requirements of exploration in underground special space. Based on the graph optimization SLAM method, an optimization method for map construction is proposed. The Iterative Closest Point (ICP) algorithm is used to match two frames of laser to recalculate the relative pose of the robot, which improves the sensor utilization rate of the robot in underground space and also increase the synchronous positioning accuracy. Moreover, based on boundary cells and rapidly-exploring random tree (RRT) algorithm, a new Bio-RRT method for robot autonomous exploration is proposed in addition.

According to the experimental results, it can be seen that the upgraded SLAM method proposed in this paper achieves better results in map construction. At the same time, the algorithm presents good real-time performance as well as high accuracy and strong maintainability, particularly it can update the map continuously with the passing of time and ensure the positioning accuracy in the process of map updating. The Bio-RRT method fused with the firing excitation mechanism of boundary cells has a more purposeful random tree growth. The number of random tree expansion nodes is less, and the amount of information to be processed is reduced, which leads to the path planning time shorter and the efficiency higher. In addition, the target bias makes the random tree grow directly toward the target point with a certain probability, and the obtained path nodes are basically distributed on or on both sides of the line between the initial point and the target point, which makes the path length shorter and reduces the moving cost of the mobile robot. The final experimental results demonstrate that the proposed upgraded SLAM and Bio-RRT methods can better complete the underground special space exploration task.

Based on the background of robot autonomous exploration in underground special space, a new bio-inspired mobile robot structure with mapping and autonomous exploration algorithm is proposed in this paper. The robot structure is constructed, and the perceptual unit, control unit, driving unit and communication unit are described in detail. The robot can satisfy the practical requirements of exploring the underground dark and multiintersection space. Then, the upgraded graph optimization laser SLAM algorithm and interframe matching optimization method are proposed in this paper. The Bio-RRT independent exploration method is finally proposed, which takes shorter time in equally open space and the search strategy for multiintersection space is more efficient. The experimental results demonstrate that the proposed upgrade SLAM and Bio-RRT methods can better complete the underground space exploration task.

This paper aims to propose a novel active SLAM framework to realize avoid obstacles and finish the autonomous navigation in indoor environment.

The improved fuzzy optimized Q-Learning (FOQL) algorithm is used to solve the avoidance obstacles problem of the robot in the environment. To reduce the motion deviation of the robot, fractional controller is designed. The localization of the robot is based on FastSLAM algorithm.

Simulation results of avoiding obstacles using traditional Q-learning algorithm, optimized Q-learning algorithm and FOQL algorithm are compared. The simulation results show that the improved FOQL algorithm has a faster learning speed than other two algorithms. To verify the simulation result, the FOQL algorithm is implemented on a NAO robot and the experimental results demonstrate that the improved fuzzy optimized Q-Learning obstacle avoidance algorithm is feasible and effective.

The improved fuzzy optimized Q-Learning (FOQL) algorithm is used to solve the avoidance obstacles problem of the robot in the environment. To reduce the motion deviation of the robot, fractional controller is designed. To verify the simulation result, the FOQL algorithm is implemented on a NAO robot and the experimental results demonstrate that the improved fuzzy optimized Q-Learning obstacle avoidance algorithm is feasible and effective.

The purpose of this study is to solve the problem of an unknown initial position in a multi-robot raster map fusion. The method includes two-dimensional (2D) raster maps and three-dimensional (3D) point cloud maps.

A fusion method using multiple algorithms was proposed. For 2D raster maps, this method uses accelerated robust feature detection to extract feature points of multi-raster maps, and then feature points are matched using a two-step algorithm of minimum Euclidean distance and adjacent feature relation. Finally, the random sample consensus algorithm was used for redundant feature fusion. On the basis of 2D raster map fusion, the method of coordinate alignment is used for 3D point cloud map fusion.

To verify the effectiveness of the algorithm, the segmentation mapping method (2D raster map) and the actual robot mapping method (2D raster map and 3D point cloud map) were used for experimental verification. The experiments demonstrated the stability and reliability of the proposed algorithm.

This algorithm uses a new visual method with coordinate alignment to process the raster map, which can effectively solve the problem of the demand for the initial relative position of robots in traditional methods and be more adaptable to the fusion of 3D maps. In addition, the original data of the map can come from different types of robots, which greatly improves the universality of the algorithm.

Heterogeneous teams consisting of unmanned ground vehicles and unmanned aerial vehicles are being used for different types of missions such as surveillance, tracking and exploration. Exploration missions with heterogeneous robot teams (HeRTs) should acquire a common map for understanding the surroundings better. The purpose of this paper is to provide a unique approach with cooperative use of agents that provides a well-detailed observation over the environment where challenging details and complex structures are involved. Also, this method is suitable for real-time applications and autonomous path planning for exploration.

Lidar odometry and mapping and various similarity metrics such as Shannon entropy, Kullback–Leibler divergence, Jeffrey divergence, K divergence, Topsoe divergence, Jensen–Shannon divergence and Jensen divergence are used to construct a common height map of the environment. Furthermore, the authors presented the layering method that provides more accuracy and a better understanding of the common map.

In summary, with the experiments, the authors observed features located beneath the trees or the roofed top areas and above them without any need for global positioning system signal. Additionally, a more effective common map that enables planning trajectories for both vehicles is obtained with the determined similarity metric and the layering method.

In this study, the authors present a unique solution that implements various entropy-based similarity metrics with the aim of constructing common maps of the environment with HeRTs. To create common maps, Shannon entropy–based similarity metrics can be used, as it is the only one that holds the chain rule of conditional probability precisely. Seven distinct similarity metrics are compared, and the most effective one is chosen for getting a more comprehensive and valid common map. Moreover, different from all the studies in literature, the layering method is used to compute the similarities of each local map obtained by a HeRT. This method also provides the accuracy of the merged common map, as robots’ sight of view prevents the same observations of the environment in features such as a roofed area or trees. This novel approach can also be used in global positioning system-denied and closed environments. The results are verified with experiments.

Digital documentation techniques of tunneling excavations at archaeological sites are becoming more common. These methods, such as photogrammetry and LiDAR (Light Detection and Ranging), are able to create precise three-dimensional models of excavations to complement traditional forms of documentation with millimeter to centimeter accuracy. However, these techniques require either expensive pieces of equipment or a long processing time that can be prohibitive during short field seasons in remote areas. This article aims to determine the effectiveness of various low-cost sensors and real-time algorithms to create digital scans of archaeological excavations.

The authors used a class of algorithms called SLAM (Simultaneous Localization and Mapping) along with depth-sensing cameras. While these algorithms have largely improved over recent years, the accuracy of the results still depends on the scanning conditions. The authors developed a prototype of a scanning device and collected 3D data at a Maya archaeological site and refined the instrument in a system of natural caves. This article presents an analysis of the resulting 3D models to determine the effectiveness of the various sensors and algorithms employed.

While not as accurate as commercial LiDAR systems, the prototype presented, employing a time-of-flight depth sensor and using a feature-based SLAM algorithm, is a rapid and effective way to document archaeological contexts at a fraction of the cost.

The proposed system is easy to deploy, provides real-time results and would be particularly useful in salvage operations as well as in high-risk areas where cultural heritage is threatened.

This article compares many different low-cost scanning solutions for underground excavations, along with presenting a prototype that can be easily replicated for documentation purposes.

Visual 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.

This 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.

The 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.

This 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.