Mapping SLAM Research Articles

Research on AGV composition algorithm based on variable and complex environment

Aiming at the traditional Gmapping algorithm with constant number of particles, which lead to the failure of composition in variable and complex environments, based on SLAM mapping technology, this paper proposed an Adaptive Sampling (AS) algorithm, which increased the number of sampling particles when the fluctuation of the 2D laser point cloud is larger than a certain threshold, and the experimental results shown that the algorithm was able to reasonably utilize the system resources, and effectively enhanced the algorithm’s ability to compose maps in a variable and complex environment. In addition, in order to make the system have a better effect of building maps in complex environments, this paper also integrated the firefly algorithm (AF) with it, and utilized AF’s high aggregation ability to improve the distribution of sampling particles, which then improved the estimation ability of the filter. Verified by offline real environment experiments, the results shown that the optimized algorithm significantly improves the map building ability of the system.

Detecting Line Segments in Motion-Blurred Images With Events.

Making line segment detectors more reliable under motion blurs is one of the most important challenges for practical applications, such as visual SLAM and 3D line mapping. Existing line segment detection methods face severe performance degradation for accurately detecting and locating line segments when motion blur occurs. While event data shows strong complementary characteristics to images for minimal blur and edge awareness at high-temporal resolution, potentially beneficial for reliable line segment recognition. To robustly detect line segments over motion blurs, we propose to leverage the complementary information of images and events. Specifically, we first design a general frame-event feature fusion network to extract and fuse the detailed image textures and low-latency event edges, which consists of a channel-attention-based shallow fusion module and a self-attention-based dual hourglass module. We then utilize the state-of-the-art wireframe parsing networks to detect line segments on the fused feature map. Moreover, due to the lack of line segment detection datasets with pairwise motion-blurred images and events, we contribute two datasets, i.e., synthetic FE-Wireframe and realistic FE-Blurframe, for network training and evaluation. Extensive analyses on the component configurations demonstrate the design effectiveness of our fusion network. When compared to the state-of-the-arts, the proposed approach achieves the highest detection accuracy while maintaining comparable real-time performance. In addition to being robust to motion blur, our method also exhibits superior performance for line detection under high dynamic range scenes.

DP-A*: For Path Planing of UGV and Contactless Delivery

The unmanned logistics and distribution urgently require a large number of unmanned ground vehicles(UGVs) under the influence of the potential spread of the Coronavirus Disease 2019 (COVID-19). The path planning of UGV relies excessively on SLAM map, and has no self-optimization and learning ability for the space containing a large number of unknown obstacles. In this paper, a new dynamic parameter-A* (DP-A*) algorithm is proposed, which is based on the A* algorithm and enables the UGV to continuously optimize the path while performing the same task repeatedly. First, the original evaluation functions of the A* algorithm are modified by Q-Learning to memory the coordinates of unknown obstacle. Then, Q-table is adopted as an auxiliary guidance for recording the characteristics of environmental changes and generating heuristic factor to overcome the shortcoming of the A* algorithm. At last, the DP-A* algorithm can realize path planning in the instantaneous changing environment, record the actual situation of obstacles encountered, and gradually optimize the path in the task that needs multiple explorations. By several simulations with different characteristics, it is shown that our algorithm outperforms Q-learning, Sarsa and A* according to the evaluation criteria such as convergence speed, memory systems consume, Optimization ability of path planning and dynamic learning ability.

Computation offloading for ground robotic systems communicating over WiFi – an empirical exploration on performance and energy trade-offs

ContextRobotic systems are known to perform computation-intensive tasks with limited computational resources and battery life. Such systems might benefit from offloading heavy workloads to the Cloud; however, in some cases, this implies high network traffic that degrades performance and energy consumption.GoalIn this study, we aim at evaluating the impact of different computation offloading strategies on performance and energy consumption in the context of autonomous robots.MethodWe conduct two controlled experiments involving a robotic mission based on the Turtlebot3 robot and ROS 1. The mission consists of three tasks that are recurrent in robotics and good candidates for computation offloading in research, namely, SLAM mapping, navigation stack, and object recognition. Each of the tasks is either executed on board or offloaded in a full-factorial experiment design. The obtained measures are then statistically analyzed.ResultsThe results show that offloading the object recognition task causes a more significant decrease in resource utilization and energy consumption than both SLAM mapping and navigation. However, object recognition affects the volume of network traffic significantly to the extent that it can easily cause network congestion.ConclusionsIn the context of our experiments (i.e., those involving small-scale ground ROS-based mobile robots operating under WiFi networks), offloading object recognition is beneficial in terms of performance and energy consumption. Nevertheless, large network bandwidth needs to be available for object recognition offloading. While the image resolution and frame rate have a significant impact on not only the network traffic but also energy consumption and performance, these parameters need to be carefully set so that the results of this task can be always received in time, which is particularly crucial in real-time systems.

Capsule endoscopy robotics and SLAM technology

Gastrointestinal diseases are a relatively common disease, and the advent of endoscopic technology is important for the early detection and treatment of this disease. In order to adapt to current medical conditions, endoscopic robots that can move autonomously are known to be the trend of development. Most of the current capsule endoscopic robots use simultaneous localization and mapping SLAM technology to localize the capsule robot and create a map of the intestinal tract. his paper summarizes the classification of capsule endoscopy robots by referring to several articles on capsule endoscopy robots. Their working methods, advantages and disadvantages are analyzed by comparing the various types of capsule endoscopy robots horizontally. This paper also analyses the application of SLAM technology to capsule endoscopy robots. The future development of capsule endoscopy robot is prospected by combining the mainstream SLAM software and hardware technologies today. This provides the development direction for the future development of capsule endoscopy robot.

SLAM-based Navigation Technology for Rescue Robots in Post-disaster Scenarios

In order to solve the problems of difficulty and high-risk factor in implementing rescue after disasters, this paper designs an intelligent rescue robot autonomous navigation system based on LIDAR synchronous positioning and map building, with a view to achieving autonomous navigation of robots in complex post-disaster scenarios to complete rescue tasks. Firstly, the autonomous navigation system senses the scene by LiDAR and uses gmapping algorithms to construct a map of the post-disaster environment. Secondly, adaptive Monte Carlo localization algorithm is used to achieve robot localization based on radar and odometer data. Then the robot rescue work path is planned to use the Dijkstra algorithm. And TEB local planning path algorithm is used to control the robot. Finally, to verify the reliability of the autonomous navigation system designed in this paper, the ROS system software framework is used as the basis. The SLAM map construction, global path planning, and local real-time obstacle avoidance are tested practically under the scenario to ensure that the autonomous navigation of the mobile robot meets the requirements.

Photometric LiDAR and RGB-D Bundle Adjustment

The joint optimization of the sensor trajectory and 3D map is a crucial characteristic of Simultaneous Localization and Mapping SLAM systems. To achieve this, the gold standard is Bundle Adjustment (BA). Modern 3D LiDARs now retain higher resolutions that enable the creation of point cloud images resembling those taken by conventional cameras. Nevertheless, the typical effective global refinement techniques employed for RGB-D sensors are not widely applied to LiDARs. This paper presents a novel BA photometric strategy that accounts for both RGB-Dand LiDAR in the same way. Our work can be used on top of any SLAM/GNSS estimate to improve and refine the initial trajectory. We conducted different experiments using these two depth sensors on public benchmarks. Our results show that our system performs on par or better compared to other state-of-the-art ad-hoc SLAM/BA strategies, free from data association and without making assumptions about the environment. In addition, we present the benefit of jointly using RGB-D and LiDAR within our unified method. We finally release an open-source CUDA/C++ implementation at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/digiamm/ba_md_slam</uri> .

SLAM based vision self-navigation robot with RTAB-MAP algorithm

The classic 2D SLAM are not good enough in nowadays environment. This report uses virtual machine with Ubuntu based Slam_bot package, based on the RTAB-MAP algorithm and Vision SLAM mapping to simulate the four-wheeled robot to autonomously navigate to the target point in various environments. Also, this report introduces a RGBD-SLAM based algorithm which combines the visual and depth data to process the data collect from the sensors. This robot has many sensors like, lidar sensors, RGB vision camera and odometry sensors. To see how the RTAB-MAP algorithm with RGB-D sensor replace for the 2D SALM. As results, the robot with RGB-D and RTAB-MAP algorithms have very good performance. The results show that the navigation system can complete the navigation and localization in lots of complex situations. However, some problems still exist, the speed of the robot is not fast. This may limit the application of the self-navigation robot to a certain extent, like some emergency occasion.

Geometric Constraint-Based and Improved YOLOv5 Semantic SLAM for Dynamic Scenes

When using deep learning networks for dynamic feature rejection in SLAM systems, problems such as a priori static object motion leading to disturbed build quality and accuracy and slow system runtime are prone to occur. In this paper, based on the ORB-SLAM2 system, we propose a method based on improved YOLOv5 networks combined with geometric constraint methods for SLAM map building in dynamic environments. First, this paper uses ShuffleNetV2 to lighten the YOLOv5 network, which increases the improved network’s operation speed without reducing the accuracy. At the same time, a pyramidal scene parsing network segmentation head is added to the head part of the YOLOv5 network to achieve semantic extraction in the environment, so that the improved YOLOv5 network has both target detection and semantic segmentation functions. In order to eliminate the objects with low dynamic features in the environment, this paper adopts the method of geometric constraints to extract and eliminate the dynamic features of the low dynamic objects. By combining the improved YOLOv5 network with the geometric constraint method, the robustness of the system is improved and the interference of dynamic targets in the construction of the SLAM system map is eliminated. The test results on the TUM dataset show that, when constructing a map in a dynamic environment, compared with the traditional ORB-SLAM2 algorithm, the accuracy of map construction in a dynamic environment is significantly improved. The absolute trajectory error is reduced by 97.7% compared with ORB-SLAM2, and the relative position error is reduced by 59.7% compared with ORB-SLAM2. Compared with DynaSLAM for dynamic scenes of the same type, the accuracy of map construction is slightly improved, but the maximum increase in keyframe processing time is 94.7%.

Robust Scan Registration for Navigation in Forest Environment Using Low-Resolution LiDAR Sensors.

Automated forest machines are becoming important due to human operators' complex and dangerous working conditions, leading to a labor shortage. This study proposes a new method for robust SLAM and tree mapping using low-resolution LiDAR sensors in forestry conditions. Our method relies on tree detection to perform scan registration and pose correction using only low-resolution LiDAR sensors (16Ch, 32Ch) or narrow field of view Solid State LiDARs without additional sensory modalities like GPS or IMU. We evaluate our approach on three datasets, including two private and one public dataset, and demonstrate improved navigation accuracy, scan registration, tree localization, and tree diameter estimation compared to current approaches in forestry machine automation. Our results show that the proposed method yields robust scan registration using detected trees, outperforming generalized feature-based registration algorithms like Fast Point Feature Histogram, with an above 3 m reduction in RMSE for the 16Chanel LiDAR sensor. For Solid-State LiDAR the algorithm achieves a similar RMSE of 3.7 m. Additionally, our adaptive pre-processing and heuristic approach to tree detection increased the number of detected trees by 13% compared to the current approach of using fixed radius search parameters for pre-processing. Our automated tree trunk diameter estimation method yields a mean absolute error of 4.3 cm (RSME = 6.5 cm) for the local map and complete trajectory maps.

Path Planning and Parameter Adjustment

By building ros and gazebo simulation environment in virtual machine, path planning, navigation, positioning and SLAM mapping were carried out for Ackerman chassis car, and traffic sign recognition and lane line detection were completed by combining machine vision, so as to control the intelligent car's turning. Test the navigation parameters with TEB plug-in to obtain the best parameters. After testing, the navigation effect is ideal, with reliability and accuracy.

Navigation of mobile robots using handwritten maps Development of a method of matching handwritten map and a SLAM map

Navigation of mobile robots using handwritten maps Development of a method of matching handwritten map and a SLAM map

SLAM Mapping Method of Laser Radar for Tobacco Production Line Inspection Robot Based on Improved RBPF

SLAM Mapping Method of Laser Radar for Tobacco Production Line Inspection Robot Based on Improved RBPF

Visual SLAM Mapping Based on YOLOv5 in Dynamic Scenes

When building a map of a dynamic environment, simultaneous localization and mapping systems have problems such as poor robustness and inaccurate pose estimation. This paper proposes a new mapping method based on the ORB-SLAM2 algorithm combined with the YOLOv5 network. First, the YOLOv5 network of the tracing thread is used to detect dynamic objects of each frame, and to get keyframes with detection of dynamic information. Second, the dynamic objects of each image frame are detected using the YOLOv5 network, and the detected dynamic points are rejected. Finally, the global map is constructed using the keyframes after eliminating the highly dynamic objects. The test results using the TUM dataset show that when the map is constructed in a dynamic environment, compared with the ORB-SLAM2 algorithm, the absolute trajectory error of our algorithm is reduced by 97.8%, and the relative positional error is reduced by 59.7%. The average time consumed to track each image frame is improved by 94.7% compared to DynaSLAM. In terms of algorithmic real-time performance, this paper’s algorithm is significantly better than the comparable dynamic SLAM map-building algorithm DynaSLAM.

CamMap: Extrinsic Calibration of Non-Overlapping Cameras Based on SLAM Map Alignment

Multiple cameras have emerged as a promising technology for robots and vehicles due to their broad fields of view (FoV) and high resolution. However, there are often limited or no overlapping FoVs among cameras, bringing challenges to estimating extrinsic camera parameters. To overcome this problem, we propose CamMap: a novel 6-degree-of-freedom (DoF) extrinsic calibration pipeline. Following three operating rules, we make a multi-camera rig capture some similar image sequences individually to create sparse feature-based maps with a SLAM system. A two-stage optimization problem is formulated to align the maps and obtain the transformations between them based on bidirectional reprojection. The transformations are exactly the extrinsic parameters. Supporting diverse camera types, the pipeline is available in any texture-rich environment. It can calibrate any number of cameras simultaneously without requiring calibration patterns, synchronization, same resolution and frequency. The pipeline is evaluated on cameras with limited and no overlapping FoVs. In the experiments, we demonstrate our method's accuracy and efficiency. The absolute pose error (APE) between Kalibr and CamMap is less than 0.025.

Improved A* Path Planning Method Based on the Grid Map.

In obstacle spatial path planning, the traditional A* algorithm has the problem of too many turning points and slow search speed. With this in mind, a path planning method that improves the A* (A-Star) algorithm is proposed. The mobile robot platform was equipped with a lidar and inertial measurement unit (IMU). The Hdl_graph_slam mapping algorithm was used to construct a two-dimensional grid map, and the improved A* algorithm was used for path planning of the mobile robot. The algorithm introduced the path smoothing strategy and safety protection mechanism, and it eliminated redundant points and minimal corner points by judging whether there were obstacles in the connection of two path nodes. The algorithm effectively improved the smoothness of the path and facilitated the robot to move in the actual operation. It could avoid the wear of the robot by expanding obstacles and improving the safety performance of the robot. Subsequently, the algorithm introduced the steering cost model and the adaptive cost function to improve the search efficiency, making the search purposeful and effective. Lastly, the effectiveness of the proposed algorithm was verified by experiments. The average path search time was reduced by 13%. The average search extension node was reduced by 11%. The problems of too many turning points and slow search speed of traditional A* algorithm in path planning were improved.

Research on motion distortion optimization algorithm of laser SLAM

Aiming at the problems of inaccurate laser point cloud matching, large reference pose error and insufficient utilization of sensor observation data during 2D laser SLAM positioning and mapping, which lead to the motion distortion of lidar, a piecewise linear interpolation based method is proposed. Fusion of radar motion distortion optimization strategy and PL-ICP algorithm. The optimization algorithm first uses the piecewise linear interpolation method to fuse the data of the wheel odometer and the lidar, solve the robot pose corresponding to each laser point in the current frame of laser data, then convert all the laser points to the same coordinate system according to the solved pose, and finally the laser data scan matching is performed by the PL-ICP algorithm. In the experiment, the ICP and PL-ICP algorithms were compared for the radar data, and the SLAM algorithm equipped with the motion distortion optimization module was used for localization and mapping. The experimental results show that the motion distortion optimization algorithm effectively removes the motion distortion of low-precision lidar and improves the accuracy of mobile robot positioning and mapping.

LIDAR-BASED MULTI-SENSOR MODULE DEVELOPMENT AND ASSESSMENT OF APPLICABILITY FOR DAMAGE INVESTIGATION OF INDOOR DISASTER

Abstract. As the risk of disaster scenes increases, the number of cases of acquiring disaster scenes information using unmanned robots is increasing. Because unmanned robots can be remotely controlled, sensors such as LiDAR and optical cameras are installed so that investigators can safely observe disaster scenes and acquire information. In particular, the information that can be acquired is different depending on the sensor characteristics, and a sensor module suitable for the purpose is being developed. Accordingly, the National Disaster Management research Institute(NDMI) also developed a investigation robot capable of acquiring information on disaster sites independently. Based on 3D point cloud data, we developed a multi-sensor module and SLAM algorithm customized to the investigation robot to collect quantitative information on the damage situation. To test the performance of the independently developed multi-sensor module, SLAM mapping was performed in a disaster building reproduced like a disaster scene, and various SLAM algorithms and distance comparison were performed. As a result, PackSLAM developed in this study showed the lowest error. In the future, to increase applicability at disaster sites, more precise experiments will be conducted by conducted by establishing a rough terrain environment.

Mapping Server Collaboration Architecture Design with OpenVSLAM for Mobile Devices

SLAM technology, which is used for spatial recognition in autonomous driving and robotics, has recently emerged as an important technology to provide high-quality AR contents on mobile devices due to the spread of XR and metaverse technologies. In this paper, we designed, implemented, and verified the SLAM system that can be used on mobile devices. Mobile SLAM is composed of a stand-alone type that directly performs SLAM operation on a mobile device and a mapping server type that additionally configures a mapping server based on FastAPI to perform SLAM operation on the server and transmits data for map visualization to a mobile device. The mobile SLAM system proposed in this paper mixes the two types in order to make SLAM operation and map generation more efficient. The stand-alone type of SLAM system was configured as an Android app by porting the OpenVSLAM library to the Unity engine, and the map generation and performance were evaluated on desktop PCs and mobile devices. The mobile SLAM system in this paper is an open-source project, so it is expected to help develop AR contents based on SLAM in a mobile environment.

Camera Switching 및 차량의 주행 방향을 활용한 자율 발렛 주차

Autonomous valet parking(AVP) is a system that allows a vehicle to detect a parking space and park without any human intervention. This paper explores the efficient use of monocular visual simultaneous localization and mapping(visual-SLAM) in AVP scenarios, while also highlighting several problems arising from current AVP methods. These problems include the difficulties of performing a loop closure in narrow places and the unreliability of the estimated scale between the real-world and SLAM maps. This paper explores two methods for resolving these problems. The first solution is to perform AVP without loop closure. This can be done through a camera switching method that uses the front camera while parking, and the rear camera in return driving scenarios. The second solution involves making use of an alternative automatic parking algorithm that utilizes a car’s heading. Most automatic parking algorithms utilize monocular visual-SLAM, but it is difficult to calculate an accurate scale using this method. The alternative automatic parking algorithm using a car’s heading could eliminate these issues with scaling. Each method was tested in three different locations, returning average success rates of 80% in return driving and 93.3% in automatic parking. The methods highlighted in this paper only utilize cameras, which are inexpensive compared to other sensors and are expected to contribute to commercializing the AVP system.