Multi-robot Simultaneous Localization And Mapping Research Articles

A Review on Map-Merging Methods for Typical Map Types in Multiple-Ground-Robot SLAM Solutions.

When multiple robots are involved in the process of simultaneous localization and mapping (SLAM), a global map should be constructed by merging the local maps built by individual robots, so as to provide a better representation of the environment. Hence, the map-merging methods play a crucial rule in multi-robot systems and determine the performance of multi-robot SLAM. This paper looks into the key problem of map merging for multiple-ground-robot SLAM and reviews the typical map-merging methods for several important types of maps in SLAM applications: occupancy grid maps, feature-based maps, and topological maps. These map-merging approaches are classified based on their working mechanism or the type of features they deal with. The concepts and characteristics of these map-merging methods are elaborated in this review. The contents summarized in this paper provide insights and guidance for future multiple-ground-robot SLAM solutions.

A high efficient multi-robot simultaneous localization and mapping system using partial computing offloading assisted cloud point registration strategy

The robots using visual simultaneous localization and mapping (SLAM) system are generally experiencing excessive power consumption and suffer from depletion of battery energy during the course of working. The intensive computation necessary to complete complicated tasks is overwhelming for inexpensive mobile robots with limited on-board resources. To address this problem, a novel task offloading strategy combined with a new dense point cloud map construction method is proposed in this paper, which is firstly used for the improvement of the system especially in indoor scenes. First, we develop a novel strategy to remotely offload computation-intensive tasks to cloud center so that the tasks that could not originally be achieved locally on the resource-limited robot systems become possible. Second, a modified iterative closest point algorithm (ICP), named fitness score hierarchical ICP algorithm (FS-HICP), is developed to accelerate point cloud registration. The correctness, efficiency, and scalability of the proposed strategy are evaluated with both theoretical analysis and experimental simulations. The results show that the proposed method can effectively reduce the energy consumption while increase the computation capability and speed of the multi-robot visual SLAM system, especially in indoor environment.

다중 로봇 시스템 지도통합을 위한 최대 가중 클릭 기반 강인한 루프 폐쇄 기법

For efficient collaborations of multi-robot systems during missions, robots must estimate their poses and map the surrounding environment, which can be achieved through multi-robot SLAM (Simultaneous Localization and Mapping). Depending on the mission, the relative poses between the local coordinates of the robots, which must be inferred to generate a global map, may be unknown. The inference is made using the measurement constraints between the robot trajectories, which are often perception-derived measurements that rely on the similarity of two instances of sensor data. Due to this dependence, perceptual aliasing, which is a phenomenon of wrongly identifying two different places as the same location, may occur and produce false loop closures that lead to a catastrophic failure of the SLAM system. This study proposes a robust inter-robot loop closure selection method that reject outlier measurements by checking both consistency of the loop closure and the similarity between the sensor data associated with the loop closure. By considering these two properties, the correct loop closures can be found despite the lack of prior knowledge regarding the relative poses among robots. We demonstrate herein how this problem can be formulated as a maximum weight clique problem, in which the degree of data similarity associated with the loop closures is considered in the form of weight in the objective function. A simulation was executed to validate the method performance and the results showed that the proposed method outperforms existing methods.

Map-merging in Multi-robot Simultaneous Localization and Mapping Process Using Two Heterogeneous Ground Robots

In this article, a fast and reliable map-merging algorithm is proposed to produce a global two dimensional map of an indoor environment in a multi-robot simultaneous localization and mapping (SLAM) process. In SLAM process, to find its way in this environment, a robot should be able to determine its position relative to a map formed from its observations. To solve this complex problem, simultaneous localization and mapping methods are required. In large and complex environments, using a single robot is not reasonable because of the error accumulation and the time required. This can explain the tendency to employ multiple robots in parallel for this task. One of the challenges in the multi-robot SLAM is the map-merging problem. A centralized algorithm for map-merging is introduced in this research based on the features of local maps and without any knowledge about robots initial or relative positions. In order to validate the proposed merging algorithm, a medium scale experiment has been set up consisting of two heterogeneous mobile robots in an indoor environment equipped with laser sensors. The results indicate that the introduced algorithm shows good performance both in accuracy and fast map-merging.

Vision and laser fused SLAM in indoor environments with multi-robot system

Purpose Multi-robot laser-based simultaneous localization and mapping (SLAM) in large-scale environments is an essential but challenging issue in mobile robotics, especially in situations wherein no prior knowledge is available between robots. Moreover, the cumulative errors of every individual robot exert a serious negative effect on loop detection and map fusion. To address these problems, this paper aims to propose an efficient approach that combines laser and vision measurements. Design/methodology/approach A multi-robot visual laser-SLAM is developed to realize robust and efficient SLAM in large-scale environments; both vision and laser loop detections are integrated to detect robust loops. A method based on oriented brief (ORB) feature detection and bag of words (BoW) is developed, to ensure the robustness and computational effectiveness of the multi-robot SLAM system. A robust and efficient graph fusion algorithm is proposed to merge pose graphs from different robots. Findings The proposed method can detect loops more quickly and accurately than the laser-only SLAM, and it can fuse the submaps of each single robot to promote the efficiency, accuracy and robustness of the system. Originality/value Compared with the state of art of multi-robot SLAM approaches, the paper proposed a novel and more sophisticated approach. The vision-based and laser-based loops are integrated to realize a robust loop detection. The ORB features and BoW technologies are further utilized to gain real-time performance. Finally, random sample consensus and least-square methodologies are used to remove the outlier loops among robots.

Exploiting Symmetries to Design EKFs With Consistency Properties for Navigation and SLAM

The extended Kalman filter (EKF) is both the historical algorithm for multi-sensor fusion and still state of the art in numerous industrial applications. However, it may prove inconsistent in the presence of unobservability under a group of transformations. In this paper, we first build an alternative EKF based on an alternative nonlinear state error. This EKF is intimately related to the theory of the invariant EKF. Then, under a simple compatibility assumption between the error and the transformation group, we prove that the linearized model of the alternative EKF automatically captures the unobservable directions, and many desirable properties of the linear case then directly follow. This provides a novel fundamental result in filtering theory. We apply the theory to multi-sensor fusion for navigation, when all the sensors are attached to the vehicle and do not have access to absolute information, as typically occurs in GPS-denied environments. In the context of simultaneous localization and mapping (SLAM), Monte Carlo runs and comparisons to OC-EKF, robocentric EKF, and optimization-based smoothing algorithms (iSAM) illustrate the results. The proposed EKF is also proved to outperform standard EKF and to achieve comparable performance to iSAM on a publicly available real dataset for multi-robot SLAM.

Robot SLAM with Ad hoc wireless network adapted to search and rescue environments

An innovative multi-robot simultaneous localization and mapping (SLAM) is proposed based on a mobile Ad hoc local wireless sensor network (Ad-WSN). Multiple followed-robots equipped with the wireless link RS232/485 module act as mobile nodes, with various on-board sensors, Tp-link wireless local area network cards, and Tp-link wireless routers. The master robot with embedded industrial PC and a complete robot control system autonomously performs the SLAM task by exchanging information with multiple followed-robots by using this self-organizing mobile wireless network. The PC on the remote console can monitor multi-robot SLAM on-site and provide direct motion control of the robots. This mobile Ad-WSN complements an environment devoid of usual GPS signals for the robots performing SLAM task in search and rescue environments. In post-disaster areas, the network is usually absent or variable and the site scene is cluttered with obstacles. To adapt to such harsh situations, the proposed self-organizing mobile Ad-WSN enables robots to complete the SLAM process while improving the performances of object of interest identification and exploration area coverage. The information of localization and mapping can communicate freely among multiple robots and remote PC control center via this mobile Ad-WSN. Therefore, the autonomous master robot runs SLAM algorithms while exchanging information with multiple followed-robots and with the remote PC control center via this local WSN environment. Simulations and experiments validate the improved performances of the exploration area coverage, object marked, and loop closure, which are adapted to search and rescue post-disaster cluttered environments.

Master-followed Multiple Robots Cooperation SLAM Adapted to Search and Rescue Environment

The master-followed multiple robots interactive cooperation simultaneous localization and mapping (SLAM) schemes were designed in this paper, which adapts to search and rescue (SAR) cluttered environments. In our multi-robots SLAM, the proposed algorithm estimates each of multiple robots’ current local sub-map, in this occasion, a particle represents each of moving multi-robots, and simultaneously, also represents the pose of a motion robot. The trajectory of the robot’s movement generated a local sub-map; the sub-maps can be looked on as the particles. Each robot efficiently forms a local sub-map; the global map integrates over these local sub-maps; identifying SAR objects of interest, in which, each of multi-robots acts as local-level features collector. Once the object of interest (OOI) is detected, the location in the global map could be determined by the SLAM. The designed multi-robot SLAM architecture consists of PC remote control center, a master robot, and multi-followed robots. Through mobileRobot platform, the master robot controls multi-robots team, the multiple robots exchange information with each other, and then performs SLAM tasks; the PC remote control center can monitor multi-robot SLAM process and provide directly control for multi-robots, which guarantee robots conducting safety in harsh SAR environments. This SLAM method has significantly improved the objects identification, area coverage rate and loop-closure, and the corresponding simulations and experiments validate the significant effects.

SLAM for Humanoid Multi-Robot Active Cooperation Based on Relative Observation

The simultaneous localization and mapping (SLAM) of robot in the complex environment is a fundamental research topic for service robots. This paper presents a new humanoid multi-robot SLAM mechanism that allows robots to collaborate and localize each other in their own SLAM process. Each robot has two switchable modes: independent mode and collaborative mode. Each robot can respond to the requests of other robots and participate in chained localization of the target robot under the leadership of the organiser. We aslo discuss how to find the solution of optimal strategy for chained localization. This mechanism can improve the performance of bundle adjustment at the global level, especially when the image features are few or the results of closed loop are not ideal. The simulation results show that this method has a great effect on improving the accuracy of multi-robot localization and the efficiency of 3D mapping.

Multi-robot SLAM via Information Fusion Extended Kalman Filters

Abstract: This paper is concerned with Simultaneous Localization and Mapping (SLAM) problem with multiple mobile robots. Each robot detects landmarks and other robots, and estimates their positions by the extended Kalman filters. To achieve good estimation accuracy, an optimal information fusion technique is adapted to the multi-robot SLAM problem. This technique involves the minimization of the estimation error covariance by weighted averaging of the state estimates from the extended Kalman filters. Simulation and experimental results are included to show the effectiveness of the present method.

Recent advances on simultaneous localization and mapping for mobile robots

This paper gives recent advances made in simultaneous localization and mapping (SLAM) in the last decade. It summarizes the main contributions, insights, limitations, and solutions of the most popular SLAMs. Some important approaches that cannot be included in the above classifications are also discussed, such as pedestrian SLAM, SLAM with enriched maps, multi-robot SLAM, active SLAM, and SLAM in dynamic environments.

A Fast Algorithm for Multi-Robot SLAM

Multi-robot SLAM has become a research hotspot in robot simultaneous localization and mapping (SLAM) in recent years. In this paper, we propose multi-robot exactly sparse extended information filters algorithm (MRESEIF) to solve Multi-robot SLAM problem. MRESEIF algorithm sets the threshold to divide the features which are observed by robots into two parts. One part of features are used on observation update, while the other one are used on robots relocalization. The simulation data shows that MRESEIF algorithm can solve multi-robot SLAM problem effectively, the oberservation update time and motion update time are kept in constant range, and the accuracy of robot poses is perfect.

Simultaneous Localization and Mapping Using Rao-Blackwellized Particle Filters in Multi Robot Systems

In this paper we investigate the problem of Simultaneous Localization and Mapping (SLAM) for a multi robot system. Relaxing some assumptions that characterize related work we propose an application of Rao-Blackwellized Particle Filters (RBPF) for the purpose of cooperatively estimating SLAM posterior. We consider a realistic setup in which the robots start from unknown initial poses (relative locations are unknown too), and travel in the environment in order to build a shared representation of the latter. The robots are required to exchange a small amount of information only when a rendezvous event occurs and to measure relative poses during the meeting. As a consequence the approach also applies when using an unreliable wireless channel or short range communication technologies (bluetooth, RFId, etc.). Moreover it allows to take into account the uncertainty in relative pose measurements. The proposed technique, which constitutes a distributed solution to the multi robot SLAM problem, is further validated through simulations and experimental tests.

A Minimalistic Approach to Appearance-Based Visual SLAM

This paper presents a vision-based approach to simultaneous localization and mapping (SLAM) in indoor/outdoor environments with minimalistic sensing and computational requirements. The approach is based on a graph representation of robot poses, using a relaxation algorithm to obtain a globally consistent map. Each link corresponds to a relative measurement of the spatial relation between the two nodes it connects. The links describe the likelihood distribution of the relative pose as a Gaussian distribution. To estimate the covariance matrix for links obtained from an omnidirectional vision sensor, a novel method is introduced based on the relative similarity of neighboring images. This new method does not require the determination of distances to image features using multiple-view geometry, for example. Combined indoor and outdoor experiments demonstrate that the approach can handle different environments (without modification of the parameters), and it can cope with violations of the ldquoflat floor assumptionrdquo to some degree and scales well with increasing size of the environment, producing topologically correct and geometrically accurate maps at low computational cost. Further experiments demonstrate that the approach is also suitable for combining multiple overlapping maps, e.g., for solving the multirobot SLAM problem with unknown initial poses.

Multi-robot Simultaneous Localization and Mapping using Particle Filters

This paper describes an on-line algorithm for multi-robot simultaneous localization and mapping (SLAM). The starting point is the single-robot Rao-Blackwellized particle filter described by Hähnel et al., and three key generalizations are made. First, the particle filter is extended to handle multi-robot SLAM problems in which the initial pose of the robots is known (such as occurs when all robots start from the same location). Second, an approximation is introduced to solve the more general problem in which the initial pose of robots is not known a priori (such as occurs when the robots start from widely separated locations). In this latter case, it is assumed that pairs of robots will eventually encounter one another, thereby determining their relative pose. This relative attitude is used to initialize the filter, and subsequent observations from both robots are combined into a common map. Third and finally, a method is introduced to integrate observations collected prior to the first robot encounter, using the notion of a virtual robot travelling backwards in time. This novel approach allows one to integrate all data from all robots into a single common map.