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

Non-Metrical Navigation Through Visual Path Control

We describe a new method for wide-area, non-metrical robot navigationwhich enables useful, purposeful motion indoors. Our method has twophases: a training phase, in which a human user directs a wheeledrobot with an attached camera through an environment while occasionallysupplying textual place names; and a navigation phase in which theuser specifies goal place names (again as text), and the robot issueslow-level motion control in order to move to the specified place. We show thatdifferences in the visual-field locations and scales of features matched acrosstraining and navigation can be used to construct a simple and robust controlrule that guides the robot onto and along the training motion path.Our method uses an omnidirectional camera, requires approximateintrinsic and extrinsic camera calibration, and is capable of effective motioncontrol within an extended, minimally-prepared building environment floorplan.We give results for deployment within a single building floor with 7 rooms, 6corridor segments, and 15 distinct place names

深層強化学習を用いた動的環境下における事前知識不要なロボットナビゲーションに関する研究

Tohoku University博士（工学）thesi