Cooperative Simultaneous Localization And Mapping Research Articles

A semantic segmentation-based underwater acoustic image transmission framework for cooperative SLAM

With the development of underwater sonar detection technology, simultaneous localization and mapping (SLAM) approach has attracted much attention in underwater navigation field in recent years. But the weak detection ability of a single vehicle limits the SLAM performance in wide areas. Thereby, cooperative SLAM using multiple vehicles has become an important research direction. The key factor of cooperative SLAM is timely and efficient sonar image transmission among underwater vehicles. However, the limited bandwidth of underwater acoustic channels contradicts a large amount of sonar image data. It is essential to compress the images before transmission. Recently, deep neural networks have great value in image compression by virtue of the powerful learning ability of neural networks, but the existing sonar image compression methods based on neural network usually focus on the pixel-level information without the semantic-level information. In this paper, we propose a novel underwater acoustic transmission scheme called UAT-SSIC that includes semantic segmentation-based sonar image compression (SSIC) framework and the joint source-channel codec, to improve the accuracy of the semantic information of the reconstructed sonar image at the receiver. The SSIC framework consists of Auto-Encoder structure-based sonar image compression network, which is measured by a semantic segmentation network's residual. Considering that sonar images have the characteristics of blurred target edges, the semantic segmentation network used a special dilated convolution neural network (DiCNN) to enhance segmentation accuracy by expanding the range of receptive fields. The joint source-channel codec with unequal error protection is proposed that adjusts the power level of the transmitted data, which deal with sonar image transmission error caused by the serious underwater acoustic channel. Experiment results demonstrate that our method preserves more semantic information, with advantages over existing methods at the same compression ratio. It also improves the error tolerance and packet loss resistance of transmission.

A Storage-Saving Quadtree-Based Multibeam Bathymetry Map Representation Method

With the rapid advancement of the simultaneous localization and mapping (SLAM) technology, the collaboration of several autonomous underwater vehicles (AUVs) in large-scale seafloor imaging has become a trending topic. Electromagnetic waves are difficult to transfer underwater, the only viable method of communication is acoustic transmission, but its bandwidth is limited. Therefore, how to compress and process multibeam bathymetry maps so that AUVs can acquire maps gathered by other AUVs has become an important topic of study. This study presents a representation approach for multibeam bathymetry maps based on a quadtree structure. In comparison to the girding approach, the sparse pseudo-input Gaussian processes (SPGPs) method, and the octree-based method, the quadtree-based method suggested in this study preserves precision while compressing storage space. Experiments utilizing field data validate the performance of the proposed technique, and the method’s ability to compress storage space towards an AUV cooperative SLAM’s scenario.

GRACO: A Multimodal Dataset for Ground and Aerial Cooperative Localization and Mapping

Compared with using only a single type of robot, the use of drones and ground vehicles to jointly explore unknown areas can bring efficiency improvements. However, due to the difficulty of ground-aerial loop detection and especially the lack of ground-air datasets in large outdoor scenes, there is no good solution for ground-aerial cooperative localization technology right now. Therefore, this letter introduces a dataset to facilitate the development of multimodal Simultaneous Localization And Mapping (SLAM) as well as ground-air heterogeneous Cooperative SLAM (C-SLAM) algorithms. This dataset was collected by a group of ground and aerial robots equipped with Light Detection and Ranging (LiDAR), cameras, and Global Navigation Satellite/Inertial Navigation Systems (GNSS/INS). All sensors were well-calibrated and triggered by a self-made synchronization module, and the synchronization accuracy can reach millisecond level. The high-precision ground truth is obtained by post-processing the navigation information collected by the GNSS/INS system. There are considerable overlaps in the acquisition routes on the ground and in the air, which facilitates C-SLAM algorithms and loop detection. Also, the special installation angles in the air, and scenes such as channel and roof bring new challenges to the SLAM algorithm. We also tested several well-known algorithms on our dataset.

Multi-Domain Cooperative SLAM: The Enabler for Integrated Sensing and Communications

Simultaneous localization and mapping (SLAM) provides user tracking and environmental mapping capabilities, enabling communication systems to gain situational awareness. Advanced communication networks with ultra-wideband, multiple antennas, and a large number of connections present opportunities for deep integration of sensing and communications. First, the development of integrated sensing and communications (ISAC) is reviewed in this study, and the differences between ISAC and traditional communication are revealed. Then, efficient mechanisms for multi-domain collaborative SLAM are presented. In particular, research opportunities and challenges for cross-sensing, cross-user, cross-frequency, and cross-device SLAM mechanisms are proposed. In addition, SLAM-aided communication strategies are explicitly discussed. We prove that the multi-domain cooperative SLAM mechanisms based on hybrid sensing and crowdsourcing can considerably improve the accuracy of localization and mapping in complex multipath propagation environments through numerical analysis. Furthermore, we conduct testbed experiments to show that the proposed SLAM mechanisms can achieve decimeter-level localization and mapping accuracy in practical scenarios, thereby proving the application prospect of multi-domain collaborative SLAM in ISAC.

Cooperative Visual-SLAM System for UAV-Based Target Tracking in GPS-Denied Environments: A Target-Centric Approach

Autonomous tracking of dynamic targets by the use of Unmanned Aerial Vehicles (UAVs) is a challenging problem that has practical applications in many scenarios. In this context, a fundamental aspect that must be addressed has to do with the position estimation of aerial robots and a target to control the flight formation. For non-cooperative targets, their position must be estimated using the on-board sensors. Moreover, for estimating the position of UAVs, global position information may not always be available (GPS-denied environments). This work presents a cooperative visual-based SLAM (Simultaneous Localization and Mapping) system that allows a team of aerial robots to autonomously follow a non-cooperative target moving freely in a GPS-denied environment. One of the contributions of this work is to propose and investigate the use of a target-centric SLAM configuration to solve the estimation problem that differs from the well-known World-centric and Robot-centric SLAM configurations. In this sense, the proposed approach is supported by theoretical results obtained from an extensive nonlinear observability analysis. Additionally, a control system is proposed for maintaining a stable UAV flight formation with respect to the target as well. In this case, the stability of control laws is proved using the Lyapunov theory. Employing an extensive set of computer simulations, the proposed system demonstrated potentially to outperform other related approaches.

Unique 4-DOF Relative Pose Estimation with Six Distances for UWB/V-SLAM-Based Devices.

In this work we introduce a relative localization method that estimates the coordinate frame transformation between two devices based on distance measurements. We present a linear algorithm that calculates the relative pose in 2D or 3D with four degrees of freedom (4-DOF). This algorithm needs a minimum of five or six distance measurements, respectively, to estimate the relative pose uniquely. We use the linear algorithm in conjunction with outlier detection algorithms and as a good initial estimate for iterative least squares refinement. The proposed method outperforms other related linear methods in terms of distance measurements needed and in terms of accuracy. In comparison with a related linear algorithm in 2D, we can reduce 10% of the translation error. In contrast to the more general 6-DOF linear algorithm, our 4-DOF method reduces the minimum distances needed from ten to six and the rotation error by a factor of four at the standard deviation of our ultra-wideband (UWB) transponders. When using the same amount of measurements the orientation error and translation error are approximately reduced to a factor of ten. We validate our method with simulations and an experimental setup, where we integrate ultra-wideband (UWB) technology into simultaneous localization and mapping (SLAM)-based devices. The presented relative pose estimation method is intended for use in augmented reality applications for cooperative localization with head-mounted displays. We foresee practical use cases of this method in cooperative SLAM, where map merging is performed in the most proactive manner.

An Adaptive SVSF-SLAM Algorithm in Dynamic Environment for Cooperative Unmanned Vehicles

The aim of this paper is to accomplish the Unmanned Ground Vehicle (UGV) full self-governance by developing tools that are able to give an accurate automatic localization in unknown environment. Many techniques have been developed to make the most type of sensors solving the Simultaneous Localization and Mapping (SLAM) problem in static environment; whereas, little attention has been paid to the more realistic case of a dynamic environment. A solution to SLAM in dynamic environments would open up a vast range of potential applications. Filtering strategies play an important role to solve the SLAM problem, and are used to extract knowledge of the true states typically from noisy measurements or observations made of the system. In this context, we propose the adaptive Smooth Variable Structure Filter (SVSF) based approach to solve the cooperative SLAM problem. In our work, we introduce a covariance matrix to assess the uncertainty of the adaptive SVSF and to improve its performance and increase the number of its useful applications. The proposed algorithm is validated in real-world and the obtained results confirm the efficiency in terms of computational time and robustness.

Cooperative SLAM for multiple UGVs navigation using SVSF filter

ABSTRACTThe aim of this paper is to present a cooperative simultaneous localization and mapping (CSLAM) solution based on a laser telemeter. The proposed solution gives the opportunity to a group of unmanned ground vehicles (UGVs) to construct a large map and localize themselves without any human intervention. Many solutions proposed to solve this problem, most of them are based on the sequential probabilistic approach, based around Extended Kalman Filter (EKF) or the Rao-Blackwellized particle filter. In our work, we propose a new alternative to avoid these limitations, a novel alternative solution based on the smooth variable structure filter (SVSF) to solve the UGV SLAM problem is proposed. This version of SVSF-SLAM algorithm uses a boundary layer width vector and does not require covariance derivation. The new algorithm has been developed to implement the SVSF filter for CSLAM. Our contribution deals with adapting the SVSF to solve the CSLAM problem for multiple UGVs. The algorithms developed in this work were implemented using a swarm of mobile robots Pioneer 3–AT. Two mapping approaches, point-based and line-based, are implemented and validated experimentally using 2D laser telemeter sensors. Good results are obtained by the Cooperative SVSF-SLAM algorithm compared with the Cooperative EKF-SLAM.

Wireless sensor network-based communication for cooperative simultaneous localization and mapping

This paper presents a novel approach of using a Wireless Sensor Network (WSN) as the communication means for Multi-Robot, Cooperative, Simultaneous Localization and Mapping (CSLAM) applications investigating the associated design challenges and suggesting corresponding solutions. Although the proposed approach brings several benefits including an increased coverage and communication range, self-organization capabilities, quick deployment, and flexible architecture, the realization is interrelated with performance in terms of energy efficiency and reliability. In this respect, the applicability of the WSNs for the presented approach is investigated. Centralized and distributed map merging methods in WSN-based CSLAM are evaluated in detail and the impacts of packet delays and losses on the performance of CSLAM algorithms are shown. Additionally, the involved network congestion and contention dynamics are presented, while the effects of observation range, speed, time intervals between observations, and odometry readings on the SLAM accuracy are shown based on an extensive set of simulation studies.

An Inter-Vehicle Observations Approach to Cooperative SLAM

In this paper, a cooperative simultaneous localization and mapping (CSLAM) approach is proposed. The main idea of the proposed approach is to initialize the map of each vehicle comprising the known initial positions of collaborating vehicles, and continuing the SLAM process considering these vehicles as dynamic features (landmarks). The inter-vehicle observations and shared control data are used to predict and update the dynamic features. The proposed approach is first elucidated mathematically based on extended Kalman filter-simultaneous localization and mapping (EKF-SLAM) solution, and then, the performance gain is evaluated using simulations conducted for unmanned surface vehicles (USVs) with radar sensors. Simulation results show that this approach provides noticeable improvement versus the single-vehicle SLAM (or Mono-SLAM) in terms of localization accuracy and mapping performance.

An autonomous wireless sensor network deployment system using mobile robots for human existence detection in case of disasters

This paper presents a novel approach of using autonomous mobile robots to deploy a Wireless Sensor Network (WSN) for human existence detection in case of disasters. During WSN deployment, mobile robots perform cooperative Simultaneous Localization and Mapping (SLAM) and communicate over the WSN. The proposed system has important advantages over a human-assisted system, including autonomous deployment, aggregated intelligence, and flexibility. However, the realization of these envisaged gains depends on communication and coordination capabilities of the system. In this study, the advantages of an autonomous WSN deployment system by mobile robots, design principles and implementation related issues have been explained. In addition, simulation studies have been performed to show the effectiveness of the proposed approach considering WSN coverage, coordination strategies, and SLAM perspectives. Overall, this paper addresses the advantages of using multiple robots for WSN deployment in terms of cooperative exploration and cooperative SLAM, the benefit of simultaneously deploying wireless sensor nodes during the exploration of an unknown deployment zone and the use of WSN-based communication as an alternative communication method during exploration.

Decentralized Cooperative SLAM for Sparsely-Communicating Robot Networks: A Centralized-Equivalent Approach

Communication between robots is key to performance in cooperative multi-robot systems. In practice, communication connections for information exchange between all robots are not always guaranteed, which adds difficulty in performing state estimation. This paper examines the decentralized cooperative simultaneous localization and mapping (SLAM) problem, in which each robot is required to estimate the map and all robot states under a sparsely-communicating and dynamic network. We show how the exact, centralized-equivalent estimate can be obtained by all robots in the network in a decentralized manner even when the network is never fully connected. Furthermore, a robot only needs to consider its own knowledge of the network topology in order to detect when the centralized-equivalent estimate is obtainable. Our approach is validated through more than 250 min of hardware experiments using a team of real robots. The resulting estimates are compared against accurate groundtruth data for all robot poses and landmark positions. In addition, we examined the effects of communication range limit on our algorithm's performance.

Fusing Monocular Information in Multicamera SLAM

This paper explores the possibilities of using monocular simultaneous localization and mapping (SLAM) algorithms in systems with more than one camera. The idea is to combine in a single system the advantages of both monocular vision (bearings-only, infinite range observations but no 3-D instantaneous information) and stereovision (3-D information up to a limited range). Such a system should be able to instantaneously map nearby objects while still considering the bearing information provided by the observation of remote ones. We do this by considering each camera as an independent sensor rather than the entire set as a monolithic supersensor. The visual data are treated by monocular methods and fused by the SLAM filter. Several advantages naturally arise as interesting possibilities, such as the desynchronization of the firing of the sensors, the use of several unequal cameras, self-calibration, and cooperative SLAM with several independently moving cameras. We validate the approach with two different applications: a stereovision SLAM system with automatic self-calibration of the rig's main extrinsic parameters and a cooperative SLAM system with two independent free-moving cameras in an outdoor setting.

Predicting the Performance of Cooperative Simultaneous Localization and Mapping (C-SLAM)

In this paper we study the time evolution of the position estimates’ covariance in Cooperative Simultaneous Localization and Mapping (C-SLAM), and obtain analytical upper bounds for the positioning uncertainty. The derived bounds provide descriptions of the asymptotic positioning performance of a team of robots in a mapping task, as a function of the characteristics of the proprioceptive and exteroceptive sensors of the robots, and of the graph of relative position measurements recorded by the robots. A study of the properties of the Riccati recursion, which describes the propagation of uncertainty through time, yields (i) the guaranteed accuracy for a robot team in a given C-SLAM application, as well as (ii) the maximum expected steady-state uncertainty of the robots and landmarks, when the spatial distribution of features in the environment can be modeled by a known distribution. The theoretical results are validated both in simulation and experimentally.

An Experimental investigation of cooperative SLAM

This paper considers the problem of cooperative navigation and mapping by a heterogeneous team of multiple autonomous underwater vehicles (AUVs). We addressa form of cooperative Simultaneous Localization and Mapping (SLAM) in which only one vehicle is responsible for maintaining estimates of the map and poses for each robot. By combining inter-vehicle measurements with observations of the environment made by each vehicle, the result is a better knowledge of the poses of each robot in the group. We present results of an experiment in which three land robots perform cooperative SLAM in a large, ambiguous environment. Comparisons with ground truth are provided.