Map Merging Research Articles

Map Merging for Long-term Robotic Autonomy

Map Merging for Long-term Robotic Autonomy

Hierarchical Probabilistic Fusion Framework for Matching and Merging of 3-D Occupancy Maps

Fusing 3-D maps generated by multiple robots in real/semi-real time distributed mapping systems are addressed in this paper. A 3-D occupancy grid-based approach for mapping is utilized to satisfy the real/semi-real time and distributed operating constraints. This paper proposes a novel hierarchical probabilistic fusion framework, which consists of uncertainty modeling, map matching, transformation evaluation, and map merging. Before the fusion of maps, the map features and their uncertainties are explicitly modeled and integrated. For map matching, a two-level probabilistic map matching (PMM) algorithm is developed to include high-level structural and low-level voxel features. In the PMM, the structural uncertainty is first used to generate a coarse matching between the maps and its result is then used to improve the voxel level map matching, resulting in a more efficient and accurate matching between maps with a larger convergence basin. The relative transformation output from PMM algorithm is then evaluated based on the Mahalanobis distance, and the relative entropy filter is used subsequently to integrate the map dissimilarities more accurately, completing the map fusion process. The proposed approach is evaluated using map data collected from both simulated and real environments, and the results validate the accuracy, efficiency, and the support for larger convergence basin of the proposed 3-D occupancy map fusion framework.

Integrating SLAM and gas distribution mapping (SLAM-GDM) for real-time gas source localization

ABSTRACTGas distribution mapping (GDM) learns models of the spatial distribution of gas concentrations across 2D/3D environments, among others, for the purpose of localizing gas sources. GDM requires run-time robot positioning in order to associate measurements with locations in a global coordinate frame. Most approaches assume that the robot has perfect knowledge about its position, which does not necessarily hold in realistic scenarios. We argue that the simultaneous localization and mapping (SLAM) algorithm should be used together with GDM to allow operation in an unknown environment. This paper proposes an SLAM-GDM approach that combines Hector SLAM and Kernel DM + V through a map merging technique. We argue that Hector SLAM is suitable for the SLAM-GDM approach since it does not perform loop closure or global corrections, which in turn would require to re-compute the gas distribution map. Real-time experiments were conducted in an environment with single and multiple gas sources. The results showed that the predictions of gas source location in all trials were often correct to around 0.5–1.5 m for the large indoor area being tested. The results also verified that the proposed SLAM-GDM approach and the designed system were able to achieve real-time operation.

Simultaneous Merging Multiple Grid Maps Using the Robust Motion Averaging

Mapping in the GPS-denied environment is an important and challenging task in the field of robotics. In the large environment, mapping can be significantly accelerated by multiple robots exploring different parts of the environment. Accordingly, a key problem is how to integrate these local maps built by different robots into a single global map. In this paper, we propose an approach for simultaneous merging of multiple grid maps by the robust motion averaging. The main idea of this approach is to recover all global motions for map merging from a set of relative motions. Therefore, it firstly adopts the pair-wise map merging method to estimate relative motions for grid map pairs. To obtain as many reliable relative motions as possible, a graph-based sampling scheme is utilized to efficiently remove unreliable relative motions obtained from the pair-wise map merging. Subsequently, the accurate global motions can be recovered from the set of reliable relative motions by the motion averaging. Experimental results carried on real robot data sets demonstrate that proposed approach can achieve simultaneous merging of multiple grid maps with good performances.

Accurate Collaborative Globally-Referenced Digital Mapping with Standard GNSS

Exchange of location and sensor data among connected and automated vehicles will demand accurate global referencing of the digital maps currently being developed to aid positioning for automated driving. This paper explores the limit of such maps’ globally-referenced position accuracy when the mapping agents are equipped with low-cost Global Navigation Satellite System (GNSS) receivers performing standard code-phase-based navigation, and presents a globally-referenced electro-optical simultaneous localization and mapping pipeline, called GEOSLAM, designed to achieve this limit. The key accuracy-limiting factor is shown to be the asymptotic average of the error sources that impair standard GNSS positioning. Asymptotic statistics of each GNSS error source are analyzed through both simulation and empirical data to show that sub-50-cm accurate digital mapping is feasible in the horizontal plane after multiple mapping sessions with standard GNSS, but larger biases persist in the vertical direction. GEOSLAM achieves this accuracy by (i) incorporating standard GNSS position estimates in the visual SLAM framework, (ii) merging digital maps from multiple mapping sessions, and (iii) jointly optimizing structure and motion with respect to time-separated GNSS measurements.

Maplab: An Open Framework for Research in Visual-Inertial Mapping and Localization

Robust and accurate visual-inertial estimation is crucial to many of today's challenges in robotics. Being able to localize against a prior map and obtain accurate and driftfree pose estimates can push the applicability of such systems even further. Most of the currently available solutions, however, either focus on a single session use-case, lack localization capabilities or an end-to-end pipeline. We believe that only a complete system, combining state-of-the-art algorithms, scalable multi-session mapping tools, and a flexible user interface, can become an efficient research platform. We therefore present maplab, an open, research-oriented visual-inertial mapping framework for processing and manipulating multi-session maps, written in C++. On the one hand, maplab can be seen as a ready-to-use visual-inertial mapping and localization system. On the other hand, maplab provides the research community with a collection of multisession mapping tools that include map merging, visual-inertial batch optimization, and loop closure. Furthermore, it includes an online frontend that can create visual-inertial maps and also track a global drift-free pose within a localization map. In this paper, we present the system architecture, five use-cases, and evaluations of the system on public datasets. The source code of maplab is freely available for the benefit of the robotics research community.

Collaborative Mapping and Autonomous Parking for Multi-Story Parking Garage

We present a novel collaborative mapping and autonomous parking system for semi-structured multi-story parking garages, based on cooperative 3-D LiDAR point cloud registration and Bayesian probabilistic updating. First, an inertial-enhanced (IE) generalized iterative closest point (G-ICP) approach is presented to perform high accuracy registration for LiDAR odometry, which is loosely coupled with inertial measurement unit using multi-state extended Kalman filter fusion. Second, the IE G-ICP is utilized to reconstruct the 3-D point cloud model for each vehicle, and then the individual model maps are merged and updated into a global probabilistic 2-D grid map. A collaborative multiple layer semantic map is constructed to support autonomous parking. Finally, we propose a collaborative navigation approach for path planning when there are multiple vehicles in the parking garage through vehicle-to-vehicle communication. A global path planner is designed to explore the minimum cost path based on the semantic map, and local motion planning is performed using a random exploring algorithm for obstacle avoidance and path smoothing. Our pilot experimental evaluation provides a proof of concept for indoor autonomous parking by collaborative perception, map merging, and updating methodologies.

Distributed and collaborative monocular simultaneous localization and mapping for multi-robot systems in large-scale environments

In this article, we propose a distributed and collaborative monocular simultaneous localization and mapping system for the multi-robot system in large-scale environments, where monocular vision is the only exteroceptive sensor. Each robot estimates its pose and reconstructs the environment simultaneously using the same monocular simultaneous localization and mapping algorithm. Meanwhile, they share the results of their incremental maps by streaming keyframes through the robot operating system messages and the wireless network. Subsequently, each robot in the group can obtain the global map with high efficiency. To build the collaborative simultaneous localization and mapping architecture, two novel approaches are proposed. One is a robust relocalization method based on active loop closure, and the other is a vision-based multi-robot relative pose estimating and map merging method. The former is used to solve the problem of tracking failures when robots carry out long-term monocular simultaneous localization and mapping in large-scale environments, while the latter uses the appearance-based place recognition method to determine multi-robot relative poses and build the large-scale global map by merging each robot’s local map. Both KITTI data set and our own data set acquired by a handheld camera are used to evaluate the proposed system. Experimental results show that the proposed distributed multi-robot collaborative monocular simultaneous localization and mapping system can be used in both indoor small-scale and outdoor large-scale environments.

A New IntelliSense Strategy Based on Artificial Immune System for Multi-Robot Cooperation

In this paper, a novel intelliSense strategy based on an artificial immune system for multi-robot cooperation (MRC) is proposed. A laser range finder and camera are mounted on the robot to provide environmental information. Based on the principle of the artificial immune system, environmental information sensed by the robot is considered as an antigen while the robot is regarded as a B-cell and a possible node as an antibody. To improve exploration efficiency, the immune system is utilized in the single robot system (SRS) and multi-robotic system (MRS). Antibody-antigen affinity is calculated to choose optimal number of possible node, ensuring that the exploration path is optimal. Each robot explores independently according to affinity information, and cooperation among robots can be realized by utilizing the interactions among antibodies. In order to make the map merging strategy more persuasive, the Scale-Invariant Feature Transform (SIFT) feature is further used to identify the same region. In a real-context experiment, the proposed algorithm results in more accurate exploration, and it is verified that the proposed intelliSense strategy improves the accuracy and execution efficiency for mobile robots.

Localized real-time information on outdoor air quality at kindergartens in Oslo, Norway using low-cost sensor nodes

In Norway, children in kindergartens spend significant time outdoors under all weather conditions, and there is thus a natural concern about the quality of outdoor air. It is well known that air pollution is associated with a wide variety of adverse health impacts for children, with greater impact on children with asthma.Especially during winter and spring, kindergartens in Oslo that are situated close to streets with busy traffic, or in areas where wood burning is used for house heating, can experience many days with bad air quality. During these periods, updated information on air quality levels can help the kindergarten teachers to plan appropriate outdoor activities and thus protect children's health.We have installed 17 low-cost air quality nodes in kindergartens in Oslo. These nodes are smaller, cheaper and less complex to use than traditional equipment. Performance evaluation shows that while they are less accurate and suffer from higher uncertainty than reference equipment, they still can provide reliable coarse information about local pollution. The main challenge when using this technology is that calibration parameters might change with time depending on the atmospheric conditions. Thus, even if the sensors are calibrated a priori, once deployed, and especially if they are deployed for a long time, it is not possible to determine if a node is over- or under-estimating the concentration levels. To enhance the data from the sensors, we employed a data fusion technique that allows generating a detailed air quality map merging the data from the sensors and the data from an urban model, thus being able to offer air quality information to any location within Oslo.We arranged a focus group with the participation of local administration, kindergarten staff and parents to understand their opinion and needs related to the air quality information that was provided to the participant kindergartens. They expressed concern about the data quality but agree that having updated information on the air quality in the surroundings of kindergartens can help them to reduce children's exposure to air pollution.

Robust and Efficient Multirobot 3-D Mapping Merging With Octree-Based Occupancy Grids

Recent robotics applications require 3-D representations of the environments. In many cases, it is not feasible for a single robot to map the entire environment. In these cases, it is necessary for a team of robots to build maps independently and merge them into a single global map. In this paper, octree-based occupancy grids, which are currently the state-of-the-art 3-D map representation, are applied to the problem of multirobot mapping. Octrees allow large environments to be mapped efficiently, in terms of memory usage, while still providing sufficiently fine resolution where required. The main contribution of this work lies in the definition and validation of a system, which use map data from commonly mapped portions of the environment with registration techniques, such that maps are merged coherently despite measurement noise and error in the relative transformations between maps for experimental data sets. The system defined can then be used in a complete solution that is ported to mobile robots. The results demonstrate that octree occupancy grids are a suitable representation for multirobot 3-D mapping, but that the proposed techniques for improving erroneous transformation estimates between map frames allow multiple maps to be merged efficiently and robustly.

Merging of positive maps: A construction of various classes of positive maps on matrix algebras

For two positive maps ϕi:B(Ki)→B(Hi), i=1,2, we construct a new linear map ϕ:B(H)→B(K), where K=K1⊕K2⊕C, H=H1⊕H2⊕C, by means of some additional ingredients such as operators and functionals. We call it a merging of maps ϕ1 and ϕ2. The properties of this construction are discussed. In particular, conditions for positivity of ϕ, as well as for 2-positivity, complete positivity, optimality and indecomposability, are provided. In particular, we show that for a pair composed of 2-positive and 2-copositive maps, there is an indecomposable merging of them. One of our main results asserts, that for a canonical merging of a pair composed of completely positive and completely copositive extremal maps, their canonical merging is an exposed positive map. This result provides a wide class of new examples of exposed positive maps. As an application, new examples of entangled PPT states are described.

3-D Map Merging on Pose Graphs

In this letter, we propose an approach for merging three-dimensional maps represented as pose graphs of point clouds. Our method can effectively deal with typical distortions affecting simultaneous localization and mapping-generated maps. Traditional map merging techniques that use a single rigid body transformation to relate the reference frames of different maps. Instead, our approach achieves more accurate results by eliminating the inconsistencies resulting from distortions affecting the inputs, and can succeed in those situations where traditional approaches fail for substantial deformations. The core idea behind our solution is to localize the robot in a reference map by using the data from another map as observations. We validated our approach on publicly available datasets, and provide quantitative results that confirm its effectiveness on challenging instances of the merging problem.

Intelligent Mobile Robots

Background/Objectives: A new method is devised to build a collection of wheeled robots that are capable of communicating with each other and mapping a maze in a minimum amount of time by dividing the work between two or more robots. Methods/Statistical analysis: The design and implementation of multiple mapping robots are undertaken using Digital Magnetic Compass, Ultrasonic Sensor, and Arduino. The designed robots use a metric, world-centric approach for mapping algorithm. Robots follow the wall while continuously sending its co-ordinates to the base station. The base station or map monitor has PC with NRF module link connected with mobile robots, and the map is plotted on a GUI. The proposed approach is a low-cost robotic application to solve SLAM problem. Findings: The proposed work is an approach for mapping and exploration of mobile robots. Initially, each robot explores the environment by themselves, and later they communicate with other robots by exchanging sensor information. Magnetometer readings help the robot to estimate their relative location. The predictive models are updated by the robots based on their exploration of the environment. Based on the experiments done it is proved that mapping and map merging decisions made using multiple robots provided better performance than using some other technique which uses a single robot for exploration. The robots communicate with each other very often and verify their location to avoid false-acceptance map matches. In case the robots are about to meet at a point known as meeting point, these robots have prior information about their relative locations, and henceforth these robots can combine their data into a map named as shared map. SLAM technique is used to find out the uncertainty in the mapping of individual robots and merging the map provided by each robot. The coordination of robots and estimation of other robot's location is done using the shared map. The robots used their wireless network available with them for exchanging information with each other. In all official runs, all the robots successfully mapped, merged the maps and explored the environment. The maps produced by the robots during each run looked alike which indicates a high level of accuracy of the system. Application/Improvements: This concept can be implemented for exploration on planets or on rescue missions It can be made further into a SWARM system in which the robots has a very high level of intelligence..

Multi‐hypothesis map merging with sinogram‐based PSO for multi‐robot systems

Multi-robot map merging without inter-robot observations, common landmarks, and the knowledge of initial relative poses is a challenging problem due to lack of features or local maxima. This Letter proposes a multi-hypothesis map merging algorithm with sinogram-based particle swarm optimisation to solve the problem. Experimental results show that the proposed algorithm can merge individual robot maps much more accurately than the existing algorithms.

Multi-Robot Localization and Mapping Based on Signed Distance Functions

This publication describes a 2D Simultaneous Localization and Mapping approach applicable to multiple mobile robots. The presented strategy uses data of 2D LIDAR sensors to build a dynamic representation based on Signed Distance Functions. Novelties of the approach are a joint map built in parallel instead of occasional merging of smaller maps and the limited drift localization which requires no loop closure detection. A multi-threaded software architecture performs registration and data integration in parallel allowing for drift-reduced pose estimation of multiple robots. Experiments are provided demonstrating the application with single and multiple robot mapping using simulated data, public accessible recorded data, two actual robots operating in a comparably large area as well as a deployment of these units at the Robocup rescue league.

Technical Note: Improving the VMERGE treatment planning algorithm for rotational radiotherapy.

The authors revisit the VMERGE treatment planning algorithm by Craft et al. ["Multicriteria VMAT optimization," Med. Phys. 39, 686-696 (2012)] for arc therapy planning and propose two changes to the method that are aimed at improving the achieved trade-off between treatment time and plan quality at little additional planning time cost, while retaining other desirable properties of the original algorithm. The original VMERGE algorithm first computes an "ideal," high quality but also highly time consuming treatment plan that irradiates the patient from all possible angles in a fine angular grid with a highly modulated beam and then makes this plan deliverable within practical treatment time by an iterative fluence map merging and sequencing algorithm. We propose two changes to this method. First, we regularize the ideal plan obtained in the first step by adding an explicit constraint on treatment time. Second, we propose a different merging criterion that comprises of identifying and merging adjacent maps whose merging results in the least degradation of radiation dose. The effect of both suggested modifications is evaluated individually and jointly on clinical prostate and paraspinal cases. Details of the two cases are reported. In the authors' computational study they found that both proposed modifications, especially the regularization, yield noticeably improved treatment plans for the same treatment times than what can be obtained using the original VMERGE method. The resulting plans match the quality of 20-beam step-and-shoot IMRT plans with a delivery time of approximately 2 min.

Matching Paths in Topological Maps

Topological maps have many applications in robotics. Matching two topological maps from the same environment can be used for map merging, place detection, map evaluation and other purposes. In this paper we present an approach to match two corresponding edges from two Topology Graphs to each other based on the actual path with which the vertices of the edges are connected in the underlying 2D grid maps. We perform experiments with two artificial maps as well as with four maps from the RoboCup Rescue WorldCup 2010.

Ontological modelling of knowledge management for human–machine integrated design of ultra-precision grinding machine

ABSTRACTDespite the rapid development of computer science and information technology, an efficient human–machine integrated enterprise information system for designing complex mechatronic products is still not fully accomplished, partly because of the inharmonious communication among collaborators. Therefore, one challenge in human–machine integration is how to establish an appropriate knowledge management (KM) model to support integration and sharing of heterogeneous product knowledge. Aiming at the diversity of design knowledge, this article proposes an ontology-based model to reach an unambiguous and normative representation of knowledge. First, an ontology-based human–machine integrated design framework is described, then corresponding ontologies and sub-ontologies are established according to different purposes and scopes. Second, a similarity calculation-based ontology integration method composed of ontology mapping and ontology merging is introduced. The ontology searching-based knowledge sharing method is then developed. Finally, a case of human–machine integrated design of a large ultra-precision grinding machine is used to demonstrate the effectiveness of the method.

Merging grid maps of different resolutions by scaling registration

SUMMARYThis paper considers the problem of merging grid maps that have different resolutions. Because the goal of map merging is to find the optimal transformation between two partially overlapping grid maps, it can be viewed as a special image registration issue. To address this special issue, the solution considers the non-common areas and designs an objective function based on the trimmed mean-square error (MSE). The trimmed and scaling iterative closest point (TsICP) algorithm is then proposed to solve this well-designed objective function. As the TsICP algorithm can be proven to be locally convergent in theory, a good initial transformation should be provided. Accordingly, scale-invariant feature transform (SIFT) features are extracted for the maps to be potentially merged, and the random sample consensus (RANSAC) algorithm is employed to find the geometrically consistent feature matches that are used to estimate the initial transformation for the TsICP algorithm. In addition, this paper presents the rules for the fusion of the grid maps based on the estimated transformation. Experimental results carried out with publicly available datasets illustrate the superior performance of this approach at merging grid maps with respect to robustness and accuracy.