Map Merging Research Articles

Graph-based robust 3D point cloud map merging approach for large scale

Graph-based robust 3D point cloud map merging approach for large scale

Effective Map Merging for Long-Term Autonomous Navigation

Effective Map Merging for Long-Term Autonomous Navigation

Multi-Robot Exploration Employing Harmonic Map Transformations

Robot Exploration can be used to autonomously map an area or conduct search missions in remote or hazardous environments. Using multiple robots to perform this task can improve efficiency for time-critical applications. In this work, a distributed method for multi-robot exploration using a Harmonic Map Transformation (HMT) is presented. We employ SLAM to construct a map of the unknown area and utilize map merging to share terrain information amongst robots. Then, a frontier allocation strategy is proposed to increase efficiency. The HMT is used to safely navigate the robots to the frontiers until the exploration task is complete. We validate the efficacy of the proposed strategy via tests in simulated and real-world environments. Our method is compared to other recent schemes for multi-robot exploration and is shown to outperform them in terms of total path distance.

PAL-SLAM2: Visual and visual–inertial monocular SLAM for panoramic annular lens

This paper presents PAL-SLAM2, a visual and visual–inertial monocular simultaneous localization and mapping (SLAM) system for a panoramic annular lens (PAL) with an ultra-hemispherical field of view (FoV), overcoming the limitations of traditional frameworks in handling fast turns, nighttime conditions and rapid lighting changes. The system incorporates modules for initialization, tracking, local mapping, loop and map merging. To fully exploit information from the negative space (z<0), keypoints are projected onto the unit sphere for visual initialization and tightly-coupled visual–inertial optimization. Leveraging a multimap strategy, the feature-based PAL-SLAM2 is capable of detecting unidirectional and bidirectional public areas using PAL images, thereby enhancing the performance of loop correction and map merging. Testing indicates that it achieves an average accuracy of 7.1 cm on the PALVIO indoor dataset, surpassing similar state-of-the-art frameworks. Furthermore, we have collected a large-scale dataset comprising over 120,000 images with corresponding inertial measurement unit (IMU) data, demonstrating PAL-SLAM2’s robustness under challenging outdoor conditions. Relying solely on visual and inertial inputs, our system is particularly suited for environments where Global Navigation Satellite System (GNSS) signals are frequently obstructed, such as indoor spaces or dense urban areas. The dataset can be accessed at: https://github.com/wwendy233/Plaza-Dataset.

Adaptive magnification network for precise tumor analysis in histopathological images

The variable magnification levels in histopathology images make it difficult to accurately categorize tumor regions in breast cancer histology. In this study, a novel architecture for accurate image interpretation MagNet is presented. With specific modules like Separable Dilation Convolution (SDC), Separable Dilation Skip Block (SDSB), and Point-wise Reformation Block (PRB), MagNet uses a Parallel U-Net (PU-Net) infrastructure. SDC in the PU-Net encoder ensures downsampled generalized feature representations by capturing characteristic attributes at varying magnifications. Using feature upsampling, attribute mapping merging, and PRB for precise feature capture, the decoder improves reconstruction. While PRB combines data from several decoder levels, SDSB creates vital links between the encoder and decoder layers. MagNet requires less processing of histopathology images and improves multi-magnification feature maps. MagNet performs exceptionally well, constantly outperforming rivals in terms of accuracy (0.98), F1 score (0.97), sensitivity (0.96), and specificity (0.97). The effectiveness of MagNet and its revolutionary potential in cancer diagnostics are shown by these quantitative data.

Research on a local path planning algorithm based on multivehicle collaborative mapping and a potential field method

AbstractTo eliminate blind spots in the field of vision and achieve a safe and collision‐free path, this paper proposes a path planning method based on multivehicle collaborative mapping in the context of vehicle networking. First, a multi vehicle map merging strategy based on the fireworks algorithm is proposed. In this strategy, a dissimilarity objective function based on the concept of grid map similarity is established and an improved fireworks algorithm is used to quickly search for the maximum overlap between local maps, achieving multivehicle collaborative mapping. Second, a real‐time path planning method based on artificial potential field theory is proposed. The information obtained from multivehicle collaborative mapping is first combined with the potential field model to form a multifield coupled road environment model. Then, the obstacle repulsion potential field model is improved to address the issues of traditional artificial potential field methods that target unreachability and poor dynamic response. The feasibility and effectiveness of the collaborative path planning method and single vehicle path planning method are tested through simulation analysis. This paper demonstrates through simulation analysis that the proposed path planning method can effectively achieve beyond line of sight perception and safely and comfortably guide vehicles to complete path planning.

Communication-Aware Control of Large Data Transmissions via Centralized Cognition and 5G Networks for Multi-Robot Map merging

Multiple modern robotic applications benefit from centralized cognition and processing schemes. However, modern equipped robotic platforms can output a large amount of data, which may exceed the capabilities of modern wireless communication systems if all data is transmitted without further consideration. This research presents a multi-agent, centralized, and real-time 3D point cloud map merging scheme for ceaselessly connected robotic agents. Centralized architectures enable mission awareness to all agents at all times, making tasks such as search and rescue more effective. The centralized component is placed on an edge server, ensuring low communication latency, while all agents access the server utilizing a fifth-generation (5G) network. In addition, the proposed solution introduces a communication-aware control function that regulates the transmissions of map instances to prevent the creation of significant data congestion and communication latencies as well as address conditions where the robotic agents traverse in limited to no coverage areas. The presented framework is agnostic of the used localization and mapping procedure, while it utilizes the full power of an edge server. Finally, the efficiency of the novel established framework is being experimentally validated based on multiple scenarios.

UAVs for forestry: Metric-semantic mapping and diameter estimation with autonomous aerial robots

To properly monitor the growth of forests and administer effective methods for their cultivation, forestry researchers require access to quantitative metrics such as diameter at breast height and stem taper profile of trees. These metrics are tedious and labor-intensive to measure by hand, especially at the scale of vast forests with thick undergrowth. Autonomous mobile robots can help to scale up such operations and provide an efficient method to capture the data. We present a set of algorithms for autonomous navigation and fine-grained metric-semantic mapping with a team of aerial robots in under-canopy forest environments. Our autonomous UAV system has 3D flight capabilities and relies only on a LIDAR and an IMU for state estimation and mapping. This allows each robot to accurately navigate in challenging forest environments with drastic terrain changes regardless of illumination conditions. Our deep-learning-driven fine-grained metric-semantic mapping module is capable of detecting and extracting detailed information such as the position, orientation, and stem taper profile of trees. This map of tree trunks is represented as a set of sparse cylinder models. Our semantic place recognition module leverages this sparse representation to efficiently estimate the relative transformation between multiple robots, and merge their information to build a globally consistent large-scale map. This ultimately allows us to scale up operations with multiple robots. Our system is able to achieve a mean absolute error of 1.45 cm for diameter estimation and 13.2 cm for relative position estimation between a pair of robots after place recognition and map merging

FRAME: A Modular Framework for Autonomous Map Merging: Advancements in the Field

FRAME: A Modular Framework for Autonomous Map Merging: Advancements in the Field

Soil distribution and soil properties in the subalpine region of Kazbegi, Greater Caucasus, Georgia: Physicochemical properties, distribution and genesis

Due to their characteristic properties, mountain soils require adequate management if used for cropping or grazing, even if climatic conditions are favorable. This study provides a summary of a three-year field campaign that evaluated mountain soils in in northern Georgia for sustainable agricultural use. Soils in the Kazbegi study area, located on the northern slope of the Great Caucasian Ridge, were mapped in 2014 and 2015. The soils developed mainly on sediment fans and on glacial and fluvial sediments. They were characterized by a low to moderate yield potential, with high-yield soils found on volcanic plateaus. Despite the high diversity of terrestrial and semi-terrestrial soils, common to all of them was a high humus content. In the river valleys, Fluvisols (or at least soils with fluvic-properties) were widespread and in the valley depressions fens, e.g., Histosols, occurred as well. On volcanic substrates, medium to very deep Cambisols or Umbrisols were found, with moderate to good agricultural yield potentials. However, P-sorption, mainly on amorphous Al- and Fe-oxides, could lead to an increased need for fertilization. Cambisols were present on moraine, and primarily Regosols and Cambisols on the clay and silt-rich sedimentary rocks of the Jurassic, including a widely distributed coarse fraction. The cambic horizon was not visually apparent but was clearly detectable analytically. Depending on the slope inclination and exposure, even Leptosols occurred on the slopes and peaks of sedimentary or volcanic rocks. Based on these results, a synthetic soil map merging geological and geodetic data as well as data from field mapping was created to provide an instrument for land use management.

Optimized Target Delineation Procedure for the Radiosurgery Treatment of Ventricular Tachycardia: Observer-Independent Accuracy

Stereotactic body radiotherapy (SBRT) has shown promising results for patients with recurrent ventricular tachycardia that failed catheter ablation. The simplest method of target delineation based on manual transfer of CTV from several 3D visualizations (electroanatomic mapping system) to planning CT slices (2D) was subjective and very time consuming. Advanced methods are based on merging of electroanatomic maps (EAM) with CT. To date, 2 types of EAM-CT merge were proposed: manual alignment of EAM structures with segmented CT structures [1,2] and use of semi-automatic algorithm [3]. The goal of this work was to modify current method [2] with the use of robust automatic algorithms to assure reasonably short learning curve. This work is based on data of 10 patients who had previously undergone SBRT treatment for ventricular tachycardia (VT). Two observers participated in this study: (1) an electrophysiologist technician (cardiology) with substantial experience in EAM-CT merge, and (2) a clinical engineer (radiotherapy) with minimum experience with EAM-CT merge. Observer (1) performed EAM-CT merge three times for each case with an interval of at least 8h. Observer (2) did EAM-CT merge once for each patient following instructions written by observer 1. EAM-CT merge consist of 3 main steps: segmentation of left ventricle from CT (CT LV), registration of the CT LV and EAM, CTV delineation from EAM specific points. Mean Hausdorff distance (MHD), Dice Similarity Coefficient (DSC) and absolute difference in Center of Gravity (CoG) were used to assess intra/interobserver variability. All quantitative data was expressed as the mean and SD. Intraobserver variability Three segmented CT LVs were compared with each other. The mean DSC was 0,92±0,01 for all cases. The MHD was 1,49 ± 0,23 mm and the mean absolute difference in CoG coordinates was <1.5mm for all cases. The mean DSC and MHD for 3 CTVs altogether was 0,82±0,06 and 0,71 ±0,22 mm. The mean absolute difference in CoG was 0,64mm, 0,95mm and 0,78mm for X, Y, and Z coordinates, respectively. Interobserver variability Segmented CT LVs showed great similarity (mean DSC of 0,91±0,01, MHD of 1,86±0,47 mm) for all cases. The mean DSC comparing CTVs from both observers was 0,81±0,11 and MHD was 0,87±0,45 mm. The difference in CT LV volumes correlates with DSC of compared CTVs (Pearson´s correlation coefficient R = 0,8, p = 0,005) indicating that accurate segmentation of CT LV is crucial for successful precise CTV delineation. Modification of the current method based on incorporating automatic algorithms created a robust and fast method for CTV delineation. High similarity of both, the segmented CT LVs and delineated CTVs between observers confirmed robustness of the proposed method and steep learning curve.

Multi-Robot SLAM Using Fast LiDAR Odometry and Mapping

This paper presents an approach to implem enting centralized multirobot simultaneous localization and mapping (MR-SLAM) in an unknown environment based on LiDAR sensors. The suggested implementation addresses two main challenges faced in MR-SLAM, particularly in real-time applications: computing complexity (solving the problem with minimum time and resources) and map merging (finding the alignment between the maps and merging maps by integrating information from the aligned maps into one map). The proposed approach integrates Fast LiDAR and Odometry Mapping (FLOAM), which reduces the computational complexity of localization and mapping for individual robots by adopting a non-iterative two-stage distortion compensation method. This, in turn, accelerates inputs for the map merging algorithm and expedites the creation of a comprehensive map. The map merging algorithm utilizes feature matching techniques, Singular Value Decomposition (SVD), and the Iterative Closest Point (ICP) algorithm to estimate the transformation between the maps. Subsequently, the algorithm employs a map-merging graph to estimate the global transformation. Our system has been designed to utilize two robots and has been evaluated on datasets and in a simulated environment using ROS and Gazebo. The system required less computing time to build the global map and achieved good estimation accuracy.

Image Preprocessing with Enhanced Feature Matching for Map Merging in the Presence of Sensing Error.

Autonomous robots heavily rely on simultaneous localization and mapping (SLAM) techniques and sensor data to create accurate maps of their surroundings. When multiple robots are employed to expedite exploration, the resulting maps often have varying coordinates and scales. To achieve a comprehensive global view, the utilization of map merging techniques becomes necessary. Previous studies have typically depended on extracting image features from maps to establish connections. However, it is important to note that maps of the same location can exhibit inconsistencies due to sensing errors. Additionally, robot-generated maps are commonly represented in an occupancy grid format, which limits the availability of features for extraction and matching. Therefore, feature extraction and matching play crucial roles in map merging, particularly when dealing with uncertain sensing data. In this study, we introduce a novel method that addresses image noise resulting from sensing errors and applies additional corrections before performing feature extraction. This approach allows for the collection of features from corresponding locations in different maps, facilitating the establishment of connections between different coordinate systems and enabling effective map merging. Evaluation results demonstrate the significant reduction of sensing errors during the image stitching process, thanks to the proposed image pre-processing technique.

Hybrid intelligence strategy for techno-economic reactive power dispatch approach to ensure system security

This work consolidates chaotic maps into a contemporary heuristic approach called Chimp Optimization Algorithm (COA). Augmentation of ten chaotic maps to COA forms the hybrid Chaotic COA (CCOA) which generates solutions in search space with enhanced mobility. A bi-level strategy of validating the robustness of the proposed hybrid method is adopted by first benchmarking it on eighteen assorted benchmark test functions and later by performing statistical analysis. The result obtained candidly establishes that merging of chaotic maps (dominantly Chaotic 4 Iterative type-CCOA4) enhances the performance of COA. The inferences congregated from the benchmark functions and statistical analysis facilitates the application of the recommended algorithm to obtain techno-economic solution in reactive power dispatch (RPD) problem which is manifested as a challenging issue in power system operation. The RPD problem is analyzed implementing the proposed approach with series-shunt compensation in a competitive electricity market for secured operation and the proposed Iterative type-CCOA4 reduces the total operating cost by 3.3450 × 103$ with respect to initial operating cost.

A framework for millimeter-wave multi-user SLAM and its low-cost realization

Millimeter-wave (mmWave) is regarded as a promising technique for accurate simultaneous localization and mapping (SLAM). However, the implementation of a massive antenna array and the extensiveness of mmWave anchor deployment significantly increase the cost of the whole system, and thus mmWave is still not considered in commercial localization scenarios. In this paper, a low-cost and highly efficient mmWave-based SLAM scheme is proposed to address the above limitations. Concretely, mmWave-based sensing is leveraged to obtain the locations of obstacles. Then, multi-user landmark association and fast map merging are performed for accurate global map construction. The simulation and experimental results show that the proposed framework achieves submeter-level mapping accuracy and provides new insight into efficiently designing a SLAM system that meets the stated requirements.

Feature-Based Occupancy Map-Merging for Collaborative SLAM.

One of the most frequently used approaches to represent collaborative mapping are probabilistic occupancy grid maps. These maps can be exchanged and integrated among robots to reduce the overall exploration time, which is the main advantage of the collaborative systems. Such map fusion requires solving the unknown initial correspondence problem. This article presents an effective feature-based map fusion approach that includes processing the spatial occupancy probabilities and detecting features based on locally adaptive nonlinear diffusion filtering. We also present a procedure to verify and accept the correct transformation to avoid ambiguous map merging. Further, a global grid fusion strategy based on the Bayesian inference, which is independent of the order of merging, is also provided. It is shown that the presented method is suitable for identifying geometrically consistent features across various mapping conditions, such as low overlapping and different grid resolutions. We also present the results based on hierarchical map fusion to merge six individual maps at once in order to constrict a consistent global map for SLAM.

A Method of Merging Maps for MUAVs Based on an Improved Genetic Algorithm

The merging of environmental maps constructed by individual UAVs alone and the sharing of information are key to improving the efficiency of distributed multi-UAVexploration. This paper investigates the raster map-merging problem in the absence of a common reference coordinate system and the relative position information of UAVs, and proposes a raster map-merging method with a directed crossover multidimensional perturbation variational genetic algorithm (DCPGA). The algorithm uses an optimization function reflecting the degree of dissimilarity between the overlapping regions of two raster maps as the fitness function, with each possible rotation translation transformation corresponding to a chromosome, and the binary encoding of the coordinates as the gene string. The experimental results show that the algorithm could converge quickly and had a strong global search capability to search for the optimal overlap area of the two raster maps, thus achieving map merging.

Merging three‐dimensional occupancy grid maps to support multi‐UAVs cooperative navigation systems

AbstractUnmanned aerial vehicles (UAVs) have been widely used in many applications due to, among other features, their versatility, reduced operation cost, and reduced size. These applications increasingly require that features related to autonomous navigation be employed, such as mapping. However, the reduced capacity of resources such as battery life and hardware (memory and processing capacity) can hinder the development of these applications in UAVs. Thus, the collaborative use of multi‐UAVs for mapping can be used as an alternative to solve this problem, creating a cooperative navigation system. This system requires that individual local maps will be merged into a global map in a distributed way. In this context, this work proposes a system for merging three‐dimensional occupancy grid maps for use in UAVs that compose a cooperative navigation system. The proposed solution consists of a keypoint detector, a keypoint descriptor, and filters for keypoints and correspondences. The keypoint properties, such as orientation, are acquired from potential field gradients. Image processing techniques are applied to remove the map noise and better calculate the transformation parameters. The proposed system is validated by a set of simulation experiments performed in six different environments (indoor and outdoor). First, each component of the proposed system is individually evaluated. Then, the complete pairwise map merging system is evaluated. Finally, the appropriate functioning of the proposed system is validated through a visual evaluation.

PLUME Dashboard: A free and open-source mobile air quality monitoring dashboard

The deployment of a mobile air quality monitoring laboratory requires advanced real-time instrument monitoring and data management software, which can be prohibitively expensive. In this work we present the PLUME Dashboard: a software package built in Python designed specifically for mobile air quality monitoring purposes. It aims to provide a free and open-source alternative to comparable commercial packages, thus reducing the barrier to entry of conducting such research. This paper outlines the development of the PLUME Dashboard and justifies the design choices that were made while also providing thorough documentation and explanation for how the software works. Functionality includes real-time data display, real-time peak identification, baseline subtraction, real-time air quality and self-sampling alerts (based on wind direction and vehicle speed), and post-processing tools such as peak identification and map merging with GPS data. The functionality of PLUME Dashboard is tested using real-world data collected in Toronto and Vancouver Canada.

Robust Map Merging Method for Collaborative LiDAR-based SLAM Using GPS Sensor

Typical lidar-based mappings need to cover the entire environment alone, which often leads to long build cycles and large computational resource requirements. Collaborative simultaneous localization and mapping have been applied to explore large and complex scenarios. However, merging local maps from multiple robots remains a challenge. It is crucial for Collaborative SLAM to identify overlapping areas and obtain the transformation matrix for map merging. To this end, this paper proposes a map merging method for collaborative lidar-based SLAM based on GPS measurements and improved Iterative Closest Point (ICP). We add a GPS sensor to the traditional robot configuration and use a simplified model to convert latitude and longitude data into plane coordinate data. According to Euclidean geometry, we determine the overlapping regions scanned by robots and calculate the initial pose transformation matrix between different robots. Based on the traditional ICP algorithm, KD-tree and normal vectors are used to register point clouds in overlapping regions accurately. The transformation matrix obtained by precise registration is used to rotate and translate the source local point cloud, so as to obtain the global point cloud map. In the real-world experiment, the initial transformation matrix is successfully obtained based on GPS measurements, which provides a good initial pose for the ICP algorithm. Compared with the ICP algorithm, the time consumption and RMSE of the improved ICP algorithm are reduced by 26.3% and 7.94%, respectively. Experimental results demonstrate that the proposed method can achieve accurate and efficient map fusion for multi-robot collaborative SLAM.