State-of-the-art decentralized collaborative Simultaneous Localization And Mapping (SLAM) systems crucially lack the ability to effectively use well-mapped areas generated by other agents in the team for relocalization. This often leads to map redundancy between agents, inefficient communication, and the need for costly re-mapping of areas previously mapped by other agents. In this work, we propose a strategy to efficiently share the areas mapped by different agents in a collaborative, decentralized SLAM system. This approach directly addresses map redundancy while maintaining the consistency of the estimates across the agents and keeping the overall system scalable in terms of cross-agent communication and individual computational effort. Our method leverages covisibility information between keyframes instantiated by different agents to transfer local sub-maps on-the-fly in a completely decentralized, peer-to-peer fashion. A globally consistent estimate is achieved by solving a distributed bundle adjustment problem using the Alternating Direction Method of Multipliers (ADMM), where we enforce constraints on shared map points and keyframes across agents.

Collaborative SLAM is at the core of perception in multi-robot systems as it enables the co-localization of the team of robots in a common reference frame, which is of vital importance for any coordination amongst them. The paradigm of a centralized architecture is well established, with the robots (i.e. agents) running Visual-Inertial Odometry (VIO) onboard while communicating relevant data, such as e.g. Keyframes (KFs), to a central back-end (i.e. server), which then merges and optimizes the joint maps of the agents. While these frameworks have proven to be successful, their capability and performance are highly dependent on the choice of the VIO front-end, thus limiting their flexibility. In this work, we present COVINSG, a generalized back-end building upon the COVINS [1] framework, enabling the compatibility of the server-back-end with any arbitrary VIO front-end, including, for example, off-the-shelf cameras with odometry capabilities, such as the Realsense T265. The COVINS-G back-end deploys a multi-camera relative pose estimation algorithm for computing the loop-closure constraints allowing the system to work purely on 2D image data. In the experimental evaluation, we show on-par accuracy with state-of-the-art multi-session and collaborative SLAM systems, while demonstrating the flexibility and generality of our approach by employing different front-ends onboard collaborating agents within the same mission. The COVINS-G codebase along with a generalized front-end wrapper to allow any existing VIO front-end to be readily used in combination with the proposed collaborative back-end is open-sourced. Video- https://youtu.be/FoJfXCfaYDw

Visual-Inertial Odometry (VIO) has been widely used and researched to control and aid the automation of navigation of robots especially in the absence of absolute position measurements, such as GPS. However, when the observable landmarks in the scene lie far away, as in high-altitude flights for example, the fidelity of the metric scale estimate in VIO greatly degrades. Aiming to tackle this issue, in this work, we utilize the virtual stereo setup formed by two Unmanned Aerial Vehicles (UAVs), equipped with one camera and one Inertial Measurement Unit (IMU) each, exploiting their view overlap and relative distance measurements between them using onboard Ultra-Wideband (UWB) modules to enable collaborative VIO. In particular, we propose a decentralized collaborative estimation scheme, where each agent holds its own local map, achieving a low pose estimation latency, while ensuring consistency of each agents’ estimates via consensus-based optimization. Following a thorough evaluation in photorealistic simulations, we demonstrate the effectiveness of the approach at high-altitude flights of up to 160m, going significantly beyond the capabilities of state-of-the-art VIO methods. Finally, we show the advantage of actively adjusting the baseline on-the-fly over a fixed, target baseline, resulting in a significant reduction of the estimation error.

In this work, we present a system architecture to enable autonomous navigation of multiple agents across user-selected global interest points in a partially unknown environment. The system is composed of a server and a team of agents, here small aircrafts. Leveraging this architecture, computation-ally demanding tasks, such as global dense mapping and global path planning can be outsourced to a potentially powerful central server, limiting the onboard computation for each agent to local pose estimation using Visual-Inertial Odometry (VIO) and local path planning for obstacle avoidance. By assigning priorities to the agents, we propose a hierarchical multi-robot global planning pipeline, which avoids collisions amongst the agents and computes their paths towards the respective goals. The resulting global paths are communicated to the agents and serve as reference input to the local planner running onboard each agent. In contrast to previous works, here we relax the common assumption of a previously mapped environment and perfect knowledge about the state, and we show the effectiveness of the proposed approach in photo-realistic simulations with up to four agents operating in an industrial environment.

Driven by the promise of leveraging the benefits of collaborative robot operation, this paper presents an approach to estimate the relative transformation between two small Unmanned Aerial Vehicles (UAVs), each equipped with a single camera and an inertial sensor, comprising the first step of any meaningful collaboration. Formation flying and collaborative object manipulation are some of the few tasks that the proposed work has direct applications on, while forming a variable-baseline stereo rig using two UAVs carrying a monocular camera each promises unprecedented effectiveness in collaborative scene estimation. Assuming an overlap in the UAVs' fields of view, in the proposed framework, each UAV runs monocular-inertial odometry onboard, while an Extended Kalman Filter fuses the UAVs' estimates and common image measurements to estimate the metrically scaled relative transformation between them, in realtime. Decoupling the direction of the baseline between the cameras of the two UAVs from its magnitude, this work enables consistent and robust estimation of the uncertainty of the relative pose estimation. Our evaluation on both on simulated data and benchmarking datasets consisting of real aerial data, reveals the power of the proposed methodology in a variety of scenarios. Video - https://youtu.be/AmkkaXa2601.

Motivated by the need for globally consistent tracking and mapping before autonomous robot navigation becomes realistically feasible, this paper presents a novel backend to monocular-inertial odometry. As some of the most challenging platforms for vision-based perception, we evaluate the performance of our system using Unmanned Aerial Vehicles (UAV s). Our experimental validation demonstrates that the proposed approach achieves drift correction and metric scale estimation from a single UAV on benchmarking datasets. Furthermore, the generality of our approach is demonstrated to achieve globally consistent maps built in a collaborative manner from two UAVs, each equipped with a monocular-inertial sensor suite, showing the possible gains opened by collaboration amongst robots to perform SLAM. Video - https://youtu.be/wbX36HBu2Eg.

With robotic systems reaching considerable maturity in basic self-localization and environment mapping, new research avenues open up pushing for interaction of a robot with its surroundings for added autonomy. However, the transition from traditionally sparse feature-based maps to dense and accurate scene-estimation imperative for realistic manipulation is not straightforward. Moreover, achieving this level of scene perception in real-time from a computationally constrained and highly shaky and agile platform, such as a small an Unmanned Aerial Vehicle (UAV) is perhaps the most challenging scenario for perception for manipulation. Drawing inspiration from otherwise computationally constraining Computer Vision techniques, we present a system combining visual, inertial and depth information to achieve dense, local scene reconstruction of high precision in real-time. Our evaluation testbed is formed using ground-truth not only in the pose of the sensor-suite, but also the scene reconstruction using a highly accurate laser scanner, offering unprecedented comparisons of scene estimation to ground-truth using real sensor data. Given the lack of any real, ground-truth datasets for environment reconstruction, our V4RL Dense Surface Reconstruction dataset is publicly available.