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We present an open‐source system for Micro‐Aerial Vehicle (MAV) autonomous navigation from vision‐based sensing. Our system focuses on dense mapping, safe local planning, and global trajectory generation, especially when using narrow field‐of‐view sensors in very cluttered environments. In addition, details about other necessary parts of the system and special considerations for applications in real‐world...
Micro Aerial Vehicles (MAVs) that operate in unstructured, unexplored environments require fast and flexible local planning, which can replan when new parts of the map are explored. Trajectory optimization methods fulfill these needs, but require obstacle distance information, which can be given by Euclidean Signed Distance Fields (ESDFs). We propose a method to incrementally build ESDFs from Truncated...
Within this paper a new path planning algorithm for autonomous robotic exploration and inspection is presented. The proposed method plans online in a receding horizon fashion by sampling possible future configurations in a geometric random tree. The choice of the objective function enables the planning for either the exploration of unknown volume or inspection of a given surface manifold in both known...
Multirotor unmanned aerial vehicles (UAVs) are rapidly gaining popularity for many applications. However, safe operation in partially unknown, unstructured environments remains an open question. In this paper, we present a continuous-time trajectory optimization method for real-time collision avoidance on multirotor UAVs. We then propose a system where this motion planning method is used as a local...
As the applications of Micro Aerial Vehicles (MAVs) get more and more complex, and require highly dynamic motions, it becomes essential to have an accurate dynamic model of the MAV. Such a model can be used for reliable state estimation, control, and for realistic simulation. A good model requires accurate estimates of physical parameters of the system, which we aim to estimate from recorded flight...
This paper presents a novel path planning algorithm for the autonomous exploration of unknown space using aerial robotic platforms. The proposed planner employs a receding horizon “next-best-view” scheme: In an online computed random tree it finds the best branch, the quality of which is determined by the amount of unmapped space that can be explored. Only the first edge of this branch is executed...
Localization is essential for robots to operate autonomously, especially for extended periods of time, when estimator drift tends to destroy alignment to any global map. Though there has been extensive work in vision-based localization in recent years, including several systems that show real-time performance, none have been demonstrated running entirely on-board in closed loop on robotic platforms...
In this work, we present an MAV system that is able to relocalize itself, create consistent maps and plan paths in full 3D in previously unknown environments. This is solely based on vision and IMU measurements with all components running onboard and in real-time. We use visual-inertial odometry to keep the MAV airborne safely locally, as well as for exploration of the environment based on high-level...
High speed, low latency obstacle avoidance is essential for enabling Micro Aerial Vehicles (MAVs) to function in cluttered and dynamic environments. While other systems exist that do high-level mapping and 3D path planning for obstacle avoidance, most of these systems require high-powered CPUs on-board or off-board control from a ground station.
Recent developments in smartphones create an ideal platform for robotics and computer vision applications: they are small, powerful, embedded devices with low-power mobile CPUs. However, though the computational power of smartphones has increased substantially in recent years, they are still not capable of performing intense computer vision tasks in real time, at high frame rates and low latency.
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