Collaborative Monocular Visual SLAM for Multi-Robot

Collaborative SLAM is an amazing extension of single robot locations where multiple robots with monocular cameras work together in a dynamic environment to build one global map. The global map is later used by the multiple moving robots to localize themselves on the map. The application of collaborative SLAM can be used in various fields that include collaborative military tasks, search and rescue, agricultural planting, multi-robots working together to improve efficiency, and many others.  Generally, every existing collaborative SLAM method uses an offline technique to process the collected data in the indoor environment. The indoor environment has limited space and lacks GPS connectivity. In this paper, we aim to give a step toward the usage of two drones equipped with monocular cameras and a standard laptop as the server for monitoring indoor workplaces. We worked on Simultaneous localization and mapping standard architecture with building the centralized global SLAM by the micro aerial vehicles such as Tello in our case. We investigated the method and localization of the drone on the global map

VIR-SLAM: Visual, Inertial, and Ranging SLAM for single and multi-robot systems

Monocular cameras coupled with inertial measurements generally give high performance visual inertial odometry. However, drift can be significant with long trajectories, especially when the environment is visually challenging. In this paper, we propose a system that leverages ultra-wideband ranging with one static anchor placed in the environment to correct the accumulated error whenever the anchor is visible. We also use this setup for collaborative SLAM: different robots use mutual ranging (when available) and the common anchor to estimate the transformation between each other, facilitating map fusion Our system consists of two modules: a double layer ranging, visual, and inertial odometry for single robots, and a transformation estimation module for collaborative SLAM. We test our system on public datasets by simulating an ultra-wideband sensor as well as on real robots. Experiments show our method can outperform state-of-the-art visual-inertial odometry by more than 20%. For visually challenging environments, our method works even the visual-inertial odometry has significant drift Furthermore, we can compute the collaborative SLAM transformation matrix at almost no extra computation cost

D2D^2SLAM: Decentralized and Distributed Collaborative Visual-inertial SLAM System for Aerial Swarm

In recent years, aerial swarm technology has developed rapidly. In order to accomplish a fully autonomous aerial swarm, a key technology is decentralized and distributed collaborative SLAM (CSLAM) for aerial swarms, which estimates the relative pose and the consistent global trajectories. In this paper, we propose $D^2$SLAM: a decentralized and distributed ($D^2$) collaborative SLAM algorithm. This algorithm has high local accuracy and global consistency, and the distributed architecture allows it to scale up. $D^2$SLAM covers swarm state estimation in two scenarios: near-field state estimation for high real-time accuracy at close range and far-field state estimation for globally consistent trajectories estimation at the long-range between UAVs. Distributed optimization algorithms are adopted as the backend to achieve the $D^2$ goal. $D^2$SLAM is robust to transient loss of communication, network delays, and other factors. Thanks to the flexible architecture, $D^2$SLAM has the potential of applying in various scenarios

Collaborative SLAM using a swarm intelligence-inspired exploration method

Master's thesis in Mechatronics (MAS500)Efficient exploration in multi-robot SLAM is a challenging task. This thesis describes the design of algorithms that would enable Loomo robots to collaboratively explore an unknown environment. A pose graph-based SLAM algorithm using the on-board sensors of the Loomo was developed from scratch. A YOLOv3-tiny neural network has been trained to recognize other Loomos, and an exploration simulation has been developed to test exploration methods. The bots in the simulation are controlled using swarm intelligence inspired rules. The system is not finished, and further workis needed to combine the work done in the thesis into a collaborative SLAM system that runs on the Loomo robots

S3E: A Large-scale Multimodal Dataset for Collaborative SLAM

With the advanced request to employ a team of robots to perform a task collaboratively, the research community has become increasingly interested in collaborative simultaneous localization and mapping. Unfortunately, existing datasets are limited in the scale and variation of the collaborative trajectories, even though generalization between inter-trajectories among different agents is crucial to the overall viability of collaborative tasks. To help align the research community's contributions with realistic multiagent ordinated SLAM problems, we propose S3E, a large-scale multimodal dataset captured by a fleet of unmanned ground vehicles along four designed collaborative trajectory paradigms. S3E consists of 7 outdoor and 5 indoor sequences that each exceed 200 seconds, consisting of well temporal synchronized and spatial calibrated high-frequency IMU, high-quality stereo camera, and 360 degree LiDAR data. Crucially, our effort exceeds previous attempts regarding dataset size, scene variability, and complexity. It has 4x as much average recording time as the pioneering EuRoC dataset. We also provide careful dataset analysis as well as baselines for collaborative SLAM and single counterparts. Data and more up-to-date details are found at https://github.com/PengYu-Team/S3E

Development of Collaborative SLAM Algorithm for Team of Robots

Simultaneous Localization and Mapping (SLAM) is a fundamental problem for building truly automatic robots. Varieties of methods and algorithms have been generated, and applied into mobile robots during the last thirty years. However, each algorithm has its strength and weakness. This thesis studies the most recent published techniques in the field of mobile robot SLAM. Specifically, it focuses on investigating robot path and landmark position estimating errors made by different methods. The Hybrid method, which uses FastSLAM method as front-end and uses EKF-SLAM method as back-end, combines both methods advantages, producing smaller errors on estimating robot pose. The Hybrid method solves the single robot SLAM problems by summing the weighted mean values of each particle in FastSLAM. The contributions of this thesis is it presents an alternate mapping algorithm that extends this single-robot Hybrid SLAM algorithm to a multi-robot SLAM algorithm. In this algorithm, each robot draws map of the environment separately, and robots could transfer their mapping information into a central computer. The central computer could merge the landmark positions from different robots. At last, a revised landmark position as well as its covariance will be calculated. Landmark positions are fused together according to two robots feature information by using Kalman Filters

Collaborative Mapping of Archaeological Sites using multiple UAVs

UAVs have found an important application in archaeological mapping. Majority of the existing methods employ an offline method to process the data collected from an archaeological site. They are time-consuming and computationally expensive. In this paper, we present a multi-UAV approach for faster mapping of archaeological sites. Employing a team of UAVs not only reduces the mapping time by distribution of coverage area, but also improves the map accuracy by exchange of information. Through extensive experiments in a realistic simulation (AirSim), we demonstrate the advantages of using a collaborative mapping approach. We then create the first 3D map of the Sadra Fort, a 15th Century Fort located in Gujarat, India using our proposed method. Additionally, we present two novel archaeological datasets recorded in both simulation and real-world to facilitate research on collaborative archaeological mapping. For the benefit of the community, we make the AirSim simulation environment, as well as the datasets publicly available

Collaborative Dynamic 3D Scene Graphs for Automated Driving

Maps have played an indispensable role in enabling safe and automated driving. Although there have been many advances on different fronts ranging from SLAM to semantics, building an actionable hierarchical semantic representation of urban dynamic scenes from multiple agents is still a challenging problem. In this work, we present Collaborative URBan Scene Graphs (CURB-SG) that enable higher-order reasoning and efficient querying for many functions of automated driving. CURB-SG leverages panoptic LiDAR data from multiple agents to build large-scale maps using an effective graph-based collaborative SLAM approach that detects inter-agent loop closures. To semantically decompose the obtained 3D map, we build a lane graph from the paths of ego agents and their panoptic observations of other vehicles. Based on the connectivity of the lane graph, we segregate the environment into intersecting and non-intersecting road areas. Subsequently, we construct a multi-layered scene graph that includes lane information, the position of static landmarks and their assignment to certain map sections, other vehicles observed by the ego agents, and the pose graph from SLAM including 3D panoptic point clouds. We extensively evaluate CURB-SG in urban scenarios using a photorealistic simulator. We release our code at http://curb.cs.uni-freiburg.de.Comment: Refined manuscript and extended supplementar

Towards Collaborative Simultaneous Localization and Mapping: a Survey of the Current Research Landscape

Motivated by the tremendous progress we witnessed in recent years, this paper presents a survey of the scientific literature on the topic of Collaborative Simultaneous Localization and Mapping (C-SLAM), also known as multi-robot SLAM. With fleets of self-driving cars on the horizon and the rise of multi-robot systems in industrial applications, we believe that Collaborative SLAM will soon become a cornerstone of future robotic applications. In this survey, we introduce the basic concepts of C-SLAM and present a thorough literature review. We also outline the major challenges and limitations of C-SLAM in terms of robustness, communication, and resource management. We conclude by exploring the area's current trends and promising research avenues.Comment: 44 pages, 3 figure