SLAM Method Research Articles

MOBILE ROBOT NAVIGATION BASED ON LASER RANGE-METRY METHODS

The article is devoted to solving the problem of finding people who are under the debris of destroyed buildings, which requires an urgent solution to ensure an effective rescue operation. The primary focus of this research is the development and refinement of a navigation system for a mobile rescue robot. This paper specifically explores localization techniques and methods for constructing a map of an unknown environment, which are crucial for the robot’s effective operation in complex and dynamic settings. The overarching goal of the study is to identify the most adaptable and precise navigation method that can effectively manage the movement of a mobile robot in environments characterized by unpredictable and shifting obstacles. To solve the tasks, modern methods of laser telemetry and SLAM methods (Simultaneous Localization and Mapping) are used in the work, which are used to accurately create a map of the territory and localize the robot on it. The research proposes an innovative algorithm for the navigation system of a mobile robot, which leverages laser telemetry to meticulously scan the surrounding area. It has been shown that scanning process is essential for real-time data acquisition, enabling the robot to make informed decisions regarding its movement. It is stated that, the proposed lidar-based navigation method is particularly advantageous as it facilitates the creation of a detailed spatial map, even in environments where the layout is not known in advance. This approach allows for the precise determination of the robot’s position within the mapped space, ensuring that the robot can navigate effectively and avoid potential hazards. It is proved that, the integration of these advanced techniques significantly enhances the robot’s ability to perform in search and rescue missions, where the accurate and timely localization of trapped individuals is of paramount importance.

Static Map Construction Based on Dense Constraints and Graph Optimization

The construction of scene point-cloud maps is an important prerequisite for the registration-based localization of autonomous vehicles. In order to address the issues of the large cumulative error and low utilization efficiency of sensing information in existing SLAM methods, this paper proposes an offline static point-cloud map construction method. The key frames are described in the form of local maps, and after removing dynamic objects from the local map, it is used for inter-frame registration in a parallelized manner. The poses generated through registration are then used to construct dense constraints for global graph optimization, ultimately resulting in a global point-cloud map. The proposed method is evaluated in both simulated and real-world environments, demonstrating its feasibility.

Recent Advances in Visual SLAM Algorithms in Dynamic Environments

This paper reviews the latest research progress of visual SLAM in dynamic scenes. The improved algorithms dependent on deep learning and multi-sensor fusion are discussed. First of all, the challenges of traditional visual SLAM in dynamic environments are stated, especially the interference of high dynamic objects on system feature extraction, pose estimation and mapping. Then, the application of several object detection technologies such as YOLOv5 and YOLOv7 in dynamic visual SLAM was analyzed in detail. These methods significantly improve the positioning precision and robustness of the system by identifying and eliminating dynamic feature points in the scene. In addition, this paper explores the visual SLAM system combining semantic segmentation and geometric constraint optimization, and finally introduces the multi-sensor SLAM method combining IMU data, point cloud data and visual information. Experimental results show that these improved algorithms have a significant improvement in positioning accuracy and system robustness compared with traditional visual SLAM algorithms under TUM and other dynamic data sets.

DHDP-SLAM: Dynamic Hierarchical Dirichlet Process based data association for semantic SLAM

The key to a semantic SLAM system lies in the data association between measurements and landmarks, using the association results to provide constraints for the pose estimation of robot. However, to address the issues in data association models where data continuity and similarity are not sufficiently emphasized and single-level association strategies exhibit low robustness, we propose a data association method based on the Dynamic Hierarchical Dirichlet Process (DHDP), which is an online data association model that can make full use of the continuity and similarity between data to improve the convergence speed of the model, and at the same time, it can also dynamically take into account the influence of previous data on the current data. Additionally, DHDP has a more robust two-level association strategy to improve the accuracy of data association. In the experiments, three different datasets (Simulation dataset, KITTI dataset and TUM dataset) were selected to validate the proposed method, and the results show that DHDP has faster convergence speed and higher association accuracy, and it is able to provide additional constraints to the system when integrating it into the SLAM system, and by compared it with the state-of-the-art SLAM methods, the DHDP-SLAM exhibits higher localization accuracy.

Simultaneous Localization and Mapping Methods for Snake-like Robots Based on Gait Adjustment.

Snake robots require autonomous localization and mapping capabilities for field applications. However, the characteristics of their motion, such as large turning angles and fast rotation speeds, can lead to issues like drift or even failure in positioning and map building. In response to this situation, this paper starts from the gait motion characteristics of the snake robot itself, proposing an improved gait motion method and a tightly coupled method based on IMU and visual information to solve the problem of poor algorithm convergence caused by head-shaking in snake robot SLAM. Firstly, the adaptability of several typical gaits of the snake robot to SLAM methods was evaluated. Secondly, the serpentine gait was selected as the object of gait improvement, and a head stability control method for the snake robot was proposed, thereby reducing the interference of the snake robot's motion on the sensors. Thirdly, a visual-inertial tightly coupled SLAM method for the snake robot's serpentine gait and Arc-Rolling gait was proposed, and the method was verified to enhance the robustness of the visual SLAM algorithm and improve the positioning and mapping accuracy of the snake robot. Finally, experiments proved that the methods proposed in this paper can effectively improve the accuracy of positioning and map building for snake robots.

Robust underwater SLAM fusing bathymetric and range information

Bathymetric simultaneous localization and mapping (BSLAM) can accurately locate an autonomous underwater vehicle (AUV) in deep-sea missions, but it cannot provide real-time location results for vehicles prior to revisit due to the lack of overlap between bathymetric measurements. A real-time estimate of the vehicle’s location can not only help the vehicle to follow the given path accurately but reduce the number of invalid bathymetric associations caused by the nearly flat seafloor terrain. In this paper, we proposed a bathymetric and range information fused underwater SLAM (BRSLAM) method that estimates the vehicle’s position in real time by combining both bathymetric data and range measurements. In particular, a probabilistic graph partitioning algorithm (PGPA) was proposed to identify range outliers, and an information-theoretic node reduction method was presented to reduce the computational complexity of BRSLAM. Playback experiments showed that the BRSLAM can provide accurate navigation results with no more than 4 beacons.

SLAM Algorithm for Mobile Robots Based on Improved LVI-SAM in Complex Environments.

The foundation of robot autonomous movement is to quickly grasp the position and surroundings of the robot, which SLAM technology provides important support for. Due to the complex and dynamic environments, single-sensor SLAM methods often have the problem of degeneracy. In this paper, a multi-sensor fusion SLAM method based on the LVI-SAM framework was proposed. First of all, the state-of-the-art feature detection algorithm SuperPoint is used to extract the feature points from a visual-inertial system, enhancing the detection ability of feature points in complex scenarios. In addition, to improve the performance of loop-closure detection in complex scenarios, scan context is used to optimize the loop-closure detection. Ultimately, the experiment results show that the RMSE of the trajectory under the 05 sequence from the KITTI dataset and the Street07 sequence from the M2DGR dataset are reduced by 12% and 11%, respectively, compared to LVI-SAM. In simulated complex environments of animal farms, the error of this method at the starting and ending points of the trajectory is less than that of LVI-SAM, as well. All these experimental comparison results prove that the method proposed in this paper can achieve higher precision and robustness performance in localization and mapping within complex environments of animal farms.

Research on visual slam based on improved YOLOv5s in dynamic scenes

Abstract To address inaccuracies in camera position computations induced by dynamic objects in visual simultaneous localization and mapping (vSLAM), a method for dynamic feature point detection and rejection is given. This method merges an improved YOLOv5 lightweight network with the lucas-kanade (LK) optical flow. In the GhostNet network architecture, the typical convolutional layer is initially replaced by GhostConv separable convolution. At the end of the feature extraction network, a spatial attention mechanism based on CA is incorporated to safeguard crucial positional information and improve detection precision. This method efficiently eliminates dynamic elements, resulting in a point cloud map that is denser. Evaluations against the TUM dataset indicate that the proposed approach considerably surpasses previous SLAM methods regarding real-time efficiency and localization accuracy.

D3L-SLAM: A Comprehensive Hybrid Simultaneous Location and Mapping System with Deep Keypoint, Deep Depth, Deep Pose, and Line Detection

Robust localization and mapping are crucial for autonomous systems, but traditional handcrafted feature-based visual SLAM often struggles in challenging, textureless environments. Additionally, monocular SLAM lacks scale-aware depth perception, making accurate scene scale estimation difficult. To address these issues, we propose D3L-SLAM, a novel monocular SLAM system that integrates deep keypoints, deep depth estimates, deep pose priors, and a line detector. By leveraging deep keypoints, which are more resilient to lighting variations, our system improves the robustness of visual SLAM. We further enhance perception in low-texture areas by incorporating line features in the front-end and mitigate scale degradation with learned depth estimates. Additionally, point-line feature constraints optimize pose estimation and mapping through a tightly coupled point-line bundle adjustment (BA). The learned pose estimates refine the feature matching process during tracking, leading to more accurate localization and mapping. Experimental results on public and self-collected datasets show that D3L-SLAM significantly outperforms both traditional and learning-based visual SLAM methods in localization accuracy.

A collaborative SLAM method for dual payload-carrying UAVs in denied environments

A collaborative SLAM method for dual payload-carrying UAVs in denied environments

Rapid SLAM Method for Star Surface Rover in Unstructured Space Environments

The space environment is characterized by unstructured features, sparsity, and poor lighting conditions. The difficulty in extracting features from the visual frontend of traditional SLAM methods results in poor localization and time-consuming issues. This paper proposes a rapid and real-time localization and mapping method for star chart surveyors in unstructured space environments. Improved localization is achieved using multiple sensor fusion to sense the space environment. We replaced the traditional feature extraction module with an enhanced SuperPoint feature extraction network to tackle the challenge of challenging feature extraction in unstructured space environments. By dynamically adjusting detection thresholds, we achieved uniform detection and description of image keypoints, ultimately resulting in robust and accurate feature association information. Furthermore, we minimized redundant information to achieve precise positioning with high efficiency and low power consumption. We established a star surface rover simulation system and created simulated environments resembling Mars and the lunar surface. Compared to the LVI-SAM system, our method achieved a 20% improvement in localization accuracy for lunar scenarios. In Mars scenarios, our method achieved a positioning accuracy of 0.716 m and reduced runtime by 18.682 s for the same tasks. Our approach exhibits higher localization accuracy and lower power consumption in unstructured space environments.

DIO-SLAM: A Dynamic RGB-D SLAM Method Combining Instance Segmentation and Optical Flow.

Feature points from moving objects can negatively impact the accuracy of Visual Simultaneous Localization and Mapping (VSLAM) algorithms, while detection or semantic segmentation-based VSLAM approaches often fail to accurately determine the true motion state of objects. To address this challenge, this paper introduces DIO-SLAM: Dynamic Instance Optical Flow SLAM, a VSLAM system specifically designed for dynamic environments. Initially, the detection thread employs YOLACT (You Only Look At CoefficienTs) to distinguish between rigid and non-rigid objects within the scene. Subsequently, the optical flow thread estimates optical flow and introduces a novel approach to capture the optical flow of moving objects by leveraging optical flow residuals. Following this, an optical flow consistency method is implemented to assess the dynamic nature of rigid object mask regions, classifying them as either moving or stationary rigid objects. To mitigate errors caused by missed detections or motion blur, a motion frame propagation method is employed. Lastly, a dense mapping thread is incorporated to filter out non-rigid objects using semantic information, track the point clouds of rigid objects, reconstruct the static background, and store the resulting map in an octree format. Experimental results demonstrate that the proposed method surpasses current mainstream dynamic VSLAM techniques in both localization accuracy and real-time performance.

A Deep Analysis of Visual SLAM Methods for Highly Automated and Autonomous Vehicles in Complex Urban Environment

A Deep Analysis of Visual SLAM Methods for Highly Automated and Autonomous Vehicles in Complex Urban Environment

Real-Time Dense Visual SLAM with Neural Factor Representation

Developing a high-quality, real-time, dense visual SLAM system poses a significant challenge in the field of computer vision. NeRF introduces neural implicit representation, marking a notable advancement in visual SLAM research. However, existing neural implicit SLAM methods suffer from long runtimes and face challenges when modeling complex structures in scenes. In this paper, we propose a neural implicit dense visual SLAM method that enables high-quality real-time reconstruction even on a desktop PC. Firstly, we propose a novel neural scene representation, encoding the geometry and appearance information of the scene as a combination of the basis and coefficient factors. This representation allows for efficient memory usage and the accurate modeling of high-frequency detail regions. Secondly, we introduce feature integration rendering to significantly improve rendering speed while maintaining the quality of color rendering. Extensive experiments on synthetic and real-world datasets demonstrate that our method achieves an average improvement of more than 60% for Depth L1 and ATE RMSE compared to existing state-of-the-art methods when running at 9.8 Hz on a desktop PC with a 3.20 GHz Intel Core i9-12900K CPU and a single NVIDIA RTX 3090 GPU. This remarkable advancement highlights the crucial importance of our approach in the field of dense visual SLAM.

Visual SLAM Methods for Autonomous Driving Vehicles

Visual SLAM Methods for Autonomous Driving Vehicles

Robust Visual SLAM in Dynamic Environment Based on Motion Detection and Segmentation

Abstract In this study, we propose a robust and accurate SLAM method for dynamic environments. Our approach combines sparse optical flow with epipolar geometric constraints to detect motion, determining whether a priori dynamic objects are moving. By integrating semantic segmentation with this motion detection, we can effectively remove dynamic keypoints, eliminating the influence of dynamic objects. This dynamic object removal technique is integrated into ORB-SLAM2, en-hancing its robustness and accuracy for localization and mapping. Experimental results on the TUM dataset demonstrate that our proposed system significantly reduces pose estimation error compared to ORB-SLAM2. Specifically, the RMSE and standard deviation (S.D.) of ORB-SLAM2 are reduced by up to 97.78% and 97.91%, respectively, in highly dynamic se-quences, markedly improving robustness in dynamic environments. Furthermore, compared to other similar SLAM methods, our method reduces RMSE and S.D. by up to 69.26% and 73.03%, respectively. Dense semantic maps generated by our method also closely align with the ground truth.

VA-LOAM: Visual Assist LiDAR Odometry and Mapping for Accurate Autonomous Navigation.

In this study, we enhanced odometry performance by integrating vision sensors with LiDAR sensors, which exhibit contrasting characteristics. Vision sensors provide extensive environmental information but are limited in precise distance measurement, whereas LiDAR offers high accuracy in distance metrics but lacks detailed environmental data. By utilizing data from vision sensors, this research compensates for the inadequate descriptors of LiDAR sensors, thereby improving LiDAR feature matching performance. Traditional fusion methods, which rely on extracting depth from image features, depend heavily on vision sensors and are vulnerable under challenging conditions such as rain, darkness, or light reflection. Utilizing vision sensors as primary sensors under such conditions can lead to significant mapping errors and, in the worst cases, system divergence. Conversely, our approach uses LiDAR as the primary sensor, mitigating the shortcomings of previous methods and enabling vision sensors to support LiDAR-based mapping. This maintains LiDAR Odometry performance even in environments where vision sensors are compromised, thus enhancing performance with the support of vision sensors. We adopted five prominent algorithms from the latest LiDAR SLAM open-source projects and conducted experiments on the KITTI odometry dataset. This research proposes a novel approach by integrating a vision support module into the top three LiDAR SLAM methods, thereby improving performance. By making the source code of VA-LOAM publicly available, this work enhances the accessibility of the technology, fostering reproducibility and transparency within the research community.

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.

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.

Dense collaborative mapping with deep visual SLAM method

Abstract. The creation of highly accurate and collaborative mapping algorithms is crucial for the progress of SLAM technology, as it greatly improve the efficiency of building detailed maps. In the area of mapping based on single moving trajectory, DROID-SLAM (Differentiable Recurrent Optimization-Inspired Design) by (Teed and Deng, 2021) stands out as an innovative method based on deep learning, providing a visual-only solution that works with various types of camera, such as monocular, stereo, and RGB-D. Its ability to create maps with excellent accuracy makes it superior to well-known methods like ORB-SLAM3 by (Campos et al., 2021). Despite its impressive individual mapping performance, DROID-SLAM does not account for scenarios involving multisession data or the collaborative map creation by multiple agents. To address this problem, we propose two collaborative map construction algorithms built upon DROID-SLAM. Compared to prior methods that compute explicit relative transformations for loop closures, our algorithm leverages the power of deep learning-based bundle adjustment, using dense per-pixel correspondence, to merge into a globally consistent state. These algorithms have been thoroughly tested with stereo and RGB-D models. we validated the effectiveness of our proposed algorithms on both public and self-collected datasets, showing higher accuracy than prior methods. By leveraging the strengths of DROID-SLAM while addressing its limitations with our novel algorithms, we extend the application scenarios of this method and provide a new way of thinking about collaborative mapping.