222 research outputs found

    Search-based 3D Planning and Trajectory Optimization for Safe Micro Aerial Vehicle Flight Under Sensor Visibility Constraints

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    Safe navigation of Micro Aerial Vehicles (MAVs) requires not only obstacle-free flight paths according to a static environment map, but also the perception of and reaction to previously unknown and dynamic objects. This implies that the onboard sensors cover the current flight direction. Due to the limited payload of MAVs, full sensor coverage of the environment has to be traded off with flight time. Thus, often only a part of the environment is covered. We present a combined allocentric complete planning and trajectory optimization approach taking these sensor visibility constraints into account. The optimized trajectories yield flight paths within the apex angle of a Velodyne Puck Lite 3D laser scanner enabling low-level collision avoidance to perceive obstacles in the flight direction. Furthermore, the optimized trajectories take the flight dynamics into account and contain the velocities and accelerations along the path. We evaluate our approach with a DJI Matrice 600 MAV and in simulation employing hardware-in-the-loop.Comment: In Proceedings of IEEE International Conference on Robotics and Automation (ICRA), Montreal, Canada, May 201

    Direct Monocular Odometry Using Points and Lines

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    Most visual odometry algorithm for a monocular camera focuses on points, either by feature matching, or direct alignment of pixel intensity, while ignoring a common but important geometry entity: edges. In this paper, we propose an odometry algorithm that combines points and edges to benefit from the advantages of both direct and feature based methods. It works better in texture-less environments and is also more robust to lighting changes and fast motion by increasing the convergence basin. We maintain a depth map for the keyframe then in the tracking part, the camera pose is recovered by minimizing both the photometric error and geometric error to the matched edge in a probabilistic framework. In the mapping part, edge is used to speed up and increase stereo matching accuracy. On various public datasets, our algorithm achieves better or comparable performance than state-of-the-art monocular odometry methods. In some challenging texture-less environments, our algorithm reduces the state estimation error over 50%.Comment: ICRA 201

    Micro and macro quadcopter drones for indoor mapping to support disaster management

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    In this paper we present the operations and mapping techniques of two drones that are different in terms of size, the sensors deployed, and the positioning and mapping techniques used. The first drone is a low-cost commercial quadcopter microdrone, a Crazyflie, while the second drone is a relatively expensive research quadcopter macrodrone, called MAX. We investigated their feasibility in mapping areas where satellite positioning is not available, such as indoor spaces

    Semantic 3D Occupancy Mapping through Efficient High Order CRFs

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    Semantic 3D mapping can be used for many applications such as robot navigation and virtual interaction. In recent years, there has been great progress in semantic segmentation and geometric 3D mapping. However, it is still challenging to combine these two tasks for accurate and large-scale semantic mapping from images. In the paper, we propose an incremental and (near) real-time semantic mapping system. A 3D scrolling occupancy grid map is built to represent the world, which is memory and computationally efficient and bounded for large scale environments. We utilize the CNN segmentation as prior prediction and further optimize 3D grid labels through a novel CRF model. Superpixels are utilized to enforce smoothness and form robust P N high order potential. An efficient mean field inference is developed for the graph optimization. We evaluate our system on the KITTI dataset and improve the segmentation accuracy by 10% over existing systems.Comment: IROS 201

    MH-60 Seahawk / MQ-8 Fire Scout interoperability

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    Approved for public release; distribution is unlimitedAs part of a Naval Postgraduate School's capstone project in Systems Engineering, a project team from Cohort 311-0911 performed a Systems Engineering analysis. This Project focused on defining alternatives for enhanced Anti-Surface Warfare (ASUW) mission effectiveness through increased interoperability and integration for the Fire Scout Unmanned Air Vehicle and Seahawk helicopter. Specifically, the Project explored the available trade space for enhancing communications back to the ship for analysis and decision-making. Modeling and Simulation (MandS) was used to assess the impact of enhanced communication on specific Key performance Parameters (KPPs) and Measures of Effectiveness (MOEs) associated with the ASUW mission. Once the trade space was defined, alternatives were analyzed and a recommendation provided that supports near-, mid-, and long-term mission enhancement
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